Overview

Brought to you by YData

Dataset statistics

Number of variables53
Number of observations401125
Missing cells11588896
Missing cells (%)54.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory813.8 MiB
Average record size in memory2.1 KiB

Variable types

Numeric7
Categorical38
DateTime1
Text6
Unsupported1

Alerts

Backhoe_Mounting is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Blade_Extension is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Blade_Type is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Blade_Width is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Coupler is highly overall correlated with Coupler_SystemHigh correlation
Coupler_System is highly overall correlated with Coupler and 3 other fieldsHigh correlation
Differential_Type is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Drive_System is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Enclosure_Type is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Engine_Horsepower is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Grouser_Tracks is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Grouser_Type is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Hydraulics is highly overall correlated with ProductGroup and 3 other fieldsHigh correlation
Hydraulics_Flow is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
MachineID is highly overall correlated with datasourceHigh correlation
Pad_Type is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Pattern_Changer is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
ProductGroup is highly overall correlated with Backhoe_Mounting and 31 other fieldsHigh correlation
ProductGroupDesc is highly overall correlated with Backhoe_Mounting and 31 other fieldsHigh correlation
ProductSize is highly overall correlated with Backhoe_Mounting and 4 other fieldsHigh correlation
Pushblock is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Ride_Control is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Ripper is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
SalesID is highly overall correlated with datasourceHigh correlation
Scarifier is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Steering_Controls is highly overall correlated with Hydraulics and 3 other fieldsHigh correlation
Stick is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Stick_Length is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Thumb is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Tip_Control is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Tire_Size is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Track_Type is highly overall correlated with ProductGroup and 2 other fieldsHigh correlation
Transmission is highly overall correlated with Drive_System and 2 other fieldsHigh correlation
Travel_Controls is highly overall correlated with Hydraulics and 2 other fieldsHigh correlation
Turbocharged is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
Undercarriage_Pad_Width is highly overall correlated with ProductGroup and 1 other fieldsHigh correlation
datasource is highly overall correlated with Coupler_System and 2 other fieldsHigh correlation
Forks is highly imbalanced (62.8%)Imbalance
Pad_Type is highly imbalanced (70.4%)Imbalance
Transmission is highly imbalanced (60.6%)Imbalance
Turbocharged is highly imbalanced (71.5%)Imbalance
Blade_Extension is highly imbalanced (85.4%)Imbalance
Enclosure_Type is highly imbalanced (58.2%)Imbalance
Engine_Horsepower is highly imbalanced (71.0%)Imbalance
Coupler is highly imbalanced (57.2%)Imbalance
Coupler_System is highly imbalanced (63.5%)Imbalance
Grouser_Tracks is highly imbalanced (65.0%)Imbalance
Hydraulics_Flow is highly imbalanced (93.3%)Imbalance
Undercarriage_Pad_Width is highly imbalanced (68.2%)Imbalance
Stick_Length is highly imbalanced (70.6%)Imbalance
Pattern_Changer is highly imbalanced (72.0%)Imbalance
Grouser_Type is highly imbalanced (62.1%)Imbalance
Backhoe_Mounting is highly imbalanced (99.7%)Imbalance
Travel_Controls is highly imbalanced (72.4%)Imbalance
Differential_Type is highly imbalanced (92.5%)Imbalance
Steering_Controls is highly imbalanced (96.2%)Imbalance
auctioneerID has 20136 (5.0%) missing valuesMissing
MachineHoursCurrentMeter has 258360 (64.4%) missing valuesMissing
UsageBand has 331486 (82.6%) missing valuesMissing
fiSecondaryDesc has 137191 (34.2%) missing valuesMissing
fiModelSeries has 344217 (85.8%) missing valuesMissing
fiModelDescriptor has 329206 (82.1%) missing valuesMissing
ProductSize has 210775 (52.5%) missing valuesMissing
Drive_System has 296764 (74.0%) missing valuesMissing
Forks has 209048 (52.1%) missing valuesMissing
Pad_Type has 321991 (80.3%) missing valuesMissing
Ride_Control has 252519 (63.0%) missing valuesMissing
Stick has 321991 (80.3%) missing valuesMissing
Transmission has 217895 (54.3%) missing valuesMissing
Turbocharged has 321991 (80.3%) missing valuesMissing
Blade_Extension has 375906 (93.7%) missing valuesMissing
Blade_Width has 375906 (93.7%) missing valuesMissing
Enclosure_Type has 375906 (93.7%) missing valuesMissing
Engine_Horsepower has 375906 (93.7%) missing valuesMissing
Hydraulics has 80555 (20.1%) missing valuesMissing
Pushblock has 375906 (93.7%) missing valuesMissing
Ripper has 296988 (74.0%) missing valuesMissing
Scarifier has 375895 (93.7%) missing valuesMissing
Tip_Control has 375906 (93.7%) missing valuesMissing
Tire_Size has 306407 (76.4%) missing valuesMissing
Coupler has 187173 (46.7%) missing valuesMissing
Coupler_System has 357667 (89.2%) missing valuesMissing
Grouser_Tracks has 357763 (89.2%) missing valuesMissing
Hydraulics_Flow has 357763 (89.2%) missing valuesMissing
Track_Type has 301972 (75.3%) missing valuesMissing
Undercarriage_Pad_Width has 301253 (75.1%) missing valuesMissing
Stick_Length has 301907 (75.3%) missing valuesMissing
Thumb has 301837 (75.2%) missing valuesMissing
Pattern_Changer has 301907 (75.3%) missing valuesMissing
Grouser_Type has 301972 (75.3%) missing valuesMissing
Backhoe_Mounting has 322453 (80.4%) missing valuesMissing
Blade_Type has 321292 (80.1%) missing valuesMissing
Travel_Controls has 321291 (80.1%) missing valuesMissing
Differential_Type has 331714 (82.7%) missing valuesMissing
Steering_Controls has 331756 (82.7%) missing valuesMissing
MachineHoursCurrentMeter is highly skewed (γ1 = 36.68211443)Skewed
SalesID has unique valuesUnique
fiModelSeries is an unsupported type, check if it needs cleaning or further analysisUnsupported
MachineHoursCurrentMeter has 73126 (18.2%) zerosZeros

Reproduction

Analysis started2024-07-25 20:57:17.672077
Analysis finished2024-07-25 20:59:26.298005
Duration2 minutes and 8.63 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

SalesID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct401125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1919712.5
Minimum1139246
Maximum6333342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:26.411670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1139246
5-th percentile1208738.2
Q11418371
median1639422
Q32242707
95-th percentile4277758
Maximum6333342
Range5194096
Interquartile range (IQR)824336

Descriptive statistics

Standard deviation909021.49
Coefficient of variation (CV)0.47351959
Kurtosis10.616528
Mean1919712.5
Median Absolute Deviation (MAD)256018
Skewness3.0458144
Sum7.7004469 × 1011
Variance8.2632007 × 1011
MonotonicityStrictly increasing
2024-07-25T13:59:26.638802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1139246 1
 
< 0.1%
1804438 1
 
< 0.1%
1804468 1
 
< 0.1%
1804467 1
 
< 0.1%
1804466 1
 
< 0.1%
1804465 1
 
< 0.1%
1804463 1
 
< 0.1%
1804461 1
 
< 0.1%
1804460 1
 
< 0.1%
1804459 1
 
< 0.1%
Other values (401115) 401115
> 99.9%
ValueCountFrequency (%)
1139246 1
< 0.1%
1139248 1
< 0.1%
1139249 1
< 0.1%
1139251 1
< 0.1%
1139253 1
< 0.1%
1139255 1
< 0.1%
1139256 1
< 0.1%
1139261 1
< 0.1%
1139272 1
< 0.1%
1139275 1
< 0.1%
ValueCountFrequency (%)
6333342 1
< 0.1%
6333341 1
< 0.1%
6333338 1
< 0.1%
6333337 1
< 0.1%
6333336 1
< 0.1%
6333335 1
< 0.1%
6333311 1
< 0.1%
6333307 1
< 0.1%
6333302 1
< 0.1%
6333290 1
< 0.1%

SalePrice
Real number (ℝ)

Distinct899
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31099.713
Minimum4750
Maximum142000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:26.982603image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum4750
5-th percentile8500
Q114500
median24000
Q340000
95-th percentile81000
Maximum142000
Range137250
Interquartile range (IQR)25500

Descriptive statistics

Standard deviation23036.899
Coefficient of variation (CV)0.74074313
Kurtosis2.1978903
Mean31099.713
Median Absolute Deviation (MAD)11000
Skewness1.5251327
Sum1.2474872 × 1010
Variance5.3069869 × 108
MonotonicityNot monotonic
2024-07-25T13:59:27.173002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 7689
 
1.9%
20000 7504
 
1.9%
15000 7336
 
1.8%
26000 6988
 
1.7%
14000 6935
 
1.7%
16000 6934
 
1.7%
17000 6790
 
1.7%
10000 6700
 
1.7%
11000 6683
 
1.7%
13000 6675
 
1.7%
Other values (889) 330891
82.5%
ValueCountFrequency (%)
4750 193
 
< 0.1%
4800 19
 
< 0.1%
4850 7
 
< 0.1%
4900 26
 
< 0.1%
4935 1
 
< 0.1%
4950 3
 
< 0.1%
4987 2
 
< 0.1%
5000 817
0.2%
5100 52
 
< 0.1%
5150 1
 
< 0.1%
ValueCountFrequency (%)
142000 5
 
< 0.1%
141000 13
 
< 0.1%
140000 99
< 0.1%
139000 5
 
< 0.1%
138000 11
 
< 0.1%
137500 46
< 0.1%
137000 14
 
< 0.1%
136000 17
 
< 0.1%
135000 91
< 0.1%
134000 8
 
< 0.1%

MachineID
Real number (ℝ)

HIGH CORRELATION 

Distinct341027
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1217902.5
Minimum0
Maximum2486330
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:27.376823image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile218287.8
Q11088697
median1279490
Q31468067
95-th percentile1802909.2
Maximum2486330
Range2486330
Interquartile range (IQR)379370

Descriptive statistics

Standard deviation440991.95
Coefficient of variation (CV)0.36209134
Kurtosis1.0211359
Mean1217902.5
Median Absolute Deviation (MAD)189667
Skewness-0.73863721
Sum4.8853115 × 1011
Variance1.944739 × 1011
MonotonicityNot monotonic
2024-07-25T13:59:27.583959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2283592 26
 
< 0.1%
2285830 25
 
< 0.1%
1942724 23
 
< 0.1%
1896854 22
 
< 0.1%
2296335 20
 
< 0.1%
1746392 19
 
< 0.1%
2014324 19
 
< 0.1%
2277295 19
 
< 0.1%
2273478 18
 
< 0.1%
2268800 18
 
< 0.1%
Other values (341017) 400916
99.9%
ValueCountFrequency (%)
0 2
< 0.1%
2 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
52 1
< 0.1%
63 1
< 0.1%
66 1
< 0.1%
102 2
< 0.1%
113 1
< 0.1%
116 1
< 0.1%
ValueCountFrequency (%)
2486330 1
< 0.1%
2486276 1
< 0.1%
2486275 1
< 0.1%
2486274 1
< 0.1%
2486273 1
< 0.1%
2486111 1
< 0.1%
2486110 1
< 0.1%
2485633 1
< 0.1%
2485319 1
< 0.1%
2484957 1
< 0.1%

ModelID
Real number (ℝ)

Distinct5218
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6889.703
Minimum28
Maximum37198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:27.774247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile590
Q13259
median4604
Q38724
95-th percentile22087
Maximum37198
Range37170
Interquartile range (IQR)5465

Descriptive statistics

Standard deviation6221.7778
Coefficient of variation (CV)0.90305458
Kurtosis3.1792699
Mean6889.703
Median Absolute Deviation (MAD)2422
Skewness1.7716389
Sum2.7636321 × 109
Variance38710520
MonotonicityNot monotonic
2024-07-25T13:59:27.995516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4605 5039
 
1.3%
3538 4869
 
1.2%
3170 4315
 
1.1%
4604 4233
 
1.1%
3362 4083
 
1.0%
3537 3701
 
0.9%
3171 3442
 
0.9%
4603 3402
 
0.8%
3357 3216
 
0.8%
3178 3139
 
0.8%
Other values (5208) 361686
90.2%
ValueCountFrequency (%)
28 32
 
< 0.1%
29 9
 
< 0.1%
31 8
 
< 0.1%
34 6
 
< 0.1%
43 697
0.2%
47 238
 
0.1%
50 10
 
< 0.1%
53 56
 
< 0.1%
55 3
 
< 0.1%
75 400
0.1%
ValueCountFrequency (%)
37198 2
 
< 0.1%
37197 18
< 0.1%
37196 22
< 0.1%
36933 2
 
< 0.1%
36928 2
 
< 0.1%
36914 2
 
< 0.1%
36885 1
 
< 0.1%
36883 1
 
< 0.1%
36880 1
 
< 0.1%
36879 1
 
< 0.1%

datasource
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.0 MiB
132
260752 
136
75491 
149
26304 
121
 
23979
172
 
14599

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1203375
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row121
2nd row121
3rd row121
4th row121
5th row121

Common Values

ValueCountFrequency (%)
132 260752
65.0%
136 75491
 
18.8%
149 26304
 
6.6%
121 23979
 
6.0%
172 14599
 
3.6%

Length

2024-07-25T13:59:28.184026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:28.331430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
132 260752
65.0%
136 75491
 
18.8%
149 26304
 
6.6%
121 23979
 
6.0%
172 14599
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1 425104
35.3%
3 336243
27.9%
2 299330
24.9%
6 75491
 
6.3%
4 26304
 
2.2%
9 26304
 
2.2%
7 14599
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1203375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 425104
35.3%
3 336243
27.9%
2 299330
24.9%
6 75491
 
6.3%
4 26304
 
2.2%
9 26304
 
2.2%
7 14599
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1203375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 425104
35.3%
3 336243
27.9%
2 299330
24.9%
6 75491
 
6.3%
4 26304
 
2.2%
9 26304
 
2.2%
7 14599
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1203375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 425104
35.3%
3 336243
27.9%
2 299330
24.9%
6 75491
 
6.3%
4 26304
 
2.2%
9 26304
 
2.2%
7 14599
 
1.2%

auctioneerID
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)< 0.1%
Missing20136
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean6.5560397
Minimum0
Maximum99
Zeros407
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:28.501876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile22
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.976779
Coefficient of variation (CV)2.5894869
Kurtosis23.36608
Mean6.5560397
Median Absolute Deviation (MAD)1
Skewness4.8518094
Sum2497779
Variance288.21101
MonotonicityNot monotonic
2024-07-25T13:59:28.677992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 185310
46.2%
2 56440
 
14.1%
3 29076
 
7.2%
4 20474
 
5.1%
6 11950
 
3.0%
99 11406
 
2.8%
7 7846
 
2.0%
8 7203
 
1.8%
5 7002
 
1.7%
10 5790
 
1.4%
Other values (20) 38492
 
9.6%
(Missing) 20136
 
5.0%
ValueCountFrequency (%)
0 407
 
0.1%
1 185310
46.2%
2 56440
 
14.1%
3 29076
 
7.2%
4 20474
 
5.1%
5 7002
 
1.7%
6 11950
 
3.0%
7 7846
 
2.0%
8 7203
 
1.8%
9 4764
 
1.2%
ValueCountFrequency (%)
99 11406
2.8%
28 860
 
0.2%
27 1150
 
0.3%
26 676
 
0.2%
25 959
 
0.2%
24 1357
 
0.3%
23 1322
 
0.3%
22 1429
 
0.4%
21 1601
 
0.4%
20 2238
 
0.6%

YearMade
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1899.1569
Minimum1000
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:28.872300image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q11985
median1995
Q32000
95-th percentile2005
Maximum2013
Range1013
Interquartile range (IQR)15

Descriptive statistics

Standard deviation291.79747
Coefficient of variation (CV)0.15364579
Kurtosis5.5935115
Mean1899.1569
Median Absolute Deviation (MAD)7
Skewness-2.7535724
Sum7.6179931 × 108
Variance85145.763
MonotonicityNot monotonic
2024-07-25T13:59:29.092054image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 38185
 
9.5%
1998 21221
 
5.3%
2005 20587
 
5.1%
2004 20020
 
5.0%
1997 18905
 
4.7%
1999 18767
 
4.7%
2000 16742
 
4.2%
1996 16691
 
4.2%
1995 15528
 
3.9%
1994 14199
 
3.5%
Other values (62) 200280
49.9%
ValueCountFrequency (%)
1000 38185
9.5%
1919 127
 
< 0.1%
1920 17
 
< 0.1%
1937 1
 
< 0.1%
1942 1
 
< 0.1%
1947 1
 
< 0.1%
1948 3
 
< 0.1%
1949 1
 
< 0.1%
1950 8
 
< 0.1%
1951 7
 
< 0.1%
ValueCountFrequency (%)
2013 1
 
< 0.1%
2012 1
 
< 0.1%
2011 18
 
< 0.1%
2010 25
 
< 0.1%
2009 168
 
< 0.1%
2008 1422
 
0.4%
2007 4523
 
1.1%
2006 12215
3.0%
2005 20587
5.1%
2004 20020
5.0%

MachineHoursCurrentMeter
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct15152
Distinct (%)10.6%
Missing258360
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean3457.9554
Minimum0
Maximum2483300
Zeros73126
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-07-25T13:59:29.292185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33025
95-th percentile10419.8
Maximum2483300
Range2483300
Interquartile range (IQR)3025

Descriptive statistics

Standard deviation27590.256
Coefficient of variation (CV)7.9787775
Kurtosis1908.7846
Mean3457.9554
Median Absolute Deviation (MAD)0
Skewness36.682114
Sum4.93675 × 108
Variance7.6122225 × 108
MonotonicityNot monotonic
2024-07-25T13:59:29.509774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73126
 
18.2%
2000 122
 
< 0.1%
1000 116
 
< 0.1%
1500 101
 
< 0.1%
500 97
 
< 0.1%
800 90
 
< 0.1%
1200 87
 
< 0.1%
1400 83
 
< 0.1%
2500 83
 
< 0.1%
1700 82
 
< 0.1%
Other values (15142) 68778
 
17.1%
(Missing) 258360
64.4%
ValueCountFrequency (%)
0 73126
18.2%
2 18
 
< 0.1%
3 21
 
< 0.1%
4 35
 
< 0.1%
5 44
 
< 0.1%
6 20
 
< 0.1%
7 12
 
< 0.1%
8 18
 
< 0.1%
9 13
 
< 0.1%
10 33
 
< 0.1%
ValueCountFrequency (%)
2483300 1
< 0.1%
2202400 1
< 0.1%
1857100 1
< 0.1%
1729600 1
< 0.1%
1728600 1
< 0.1%
1711700 1
< 0.1%
1602900 1
< 0.1%
1485900 1
< 0.1%
1429800 1
< 0.1%
1282700 1
< 0.1%

UsageBand
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing331486
Missing (%)82.6%
Memory size21.8 MiB
Medium
33985 
Low
23620 
High
12034 

Length

Max length6
Median length4
Mean length4.6368558
Min length3

Characters and Unicode

Total characters322906
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowHigh
4th rowHigh
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 33985
 
8.5%
Low 23620
 
5.9%
High 12034
 
3.0%
(Missing) 331486
82.6%

Length

2024-07-25T13:59:29.719501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:29.890016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
medium 33985
48.8%
low 23620
33.9%
high 12034
 
17.3%

Most occurring characters

ValueCountFrequency (%)
i 46019
14.3%
M 33985
10.5%
e 33985
10.5%
d 33985
10.5%
u 33985
10.5%
m 33985
10.5%
L 23620
7.3%
o 23620
7.3%
w 23620
7.3%
H 12034
 
3.7%
Other values (2) 24068
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 46019
14.3%
M 33985
10.5%
e 33985
10.5%
d 33985
10.5%
u 33985
10.5%
m 33985
10.5%
L 23620
7.3%
o 23620
7.3%
w 23620
7.3%
H 12034
 
3.7%
Other values (2) 24068
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 46019
14.3%
M 33985
10.5%
e 33985
10.5%
d 33985
10.5%
u 33985
10.5%
m 33985
10.5%
L 23620
7.3%
o 23620
7.3%
w 23620
7.3%
H 12034
 
3.7%
Other values (2) 24068
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 46019
14.3%
M 33985
10.5%
e 33985
10.5%
d 33985
10.5%
u 33985
10.5%
m 33985
10.5%
L 23620
7.3%
o 23620
7.3%
w 23620
7.3%
H 12034
 
3.7%
Other values (2) 24068
7.5%
Distinct3919
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum1989-01-17 00:00:00
Maximum2011-12-30 00:00:00
2024-07-25T13:59:30.087710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:30.491770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4999
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size23.6 MiB
2024-07-25T13:59:30.955374image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length19
Median length17
Mean length4.6937887
Min length1

Characters and Unicode

Total characters1882796
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique867 ?
Unique (%)0.2%

Sample

1st row521D
2nd row950FII
3rd row226
4th rowPC120-6E
5th rowS175
ValueCountFrequency (%)
580super 5806
 
1.4%
310g 5039
 
1.2%
416c 4869
 
1.2%
580k 4315
 
1.1%
310e 4233
 
1.0%
140g 4083
 
1.0%
416b 3718
 
0.9%
l 3549
 
0.9%
580l 3444
 
0.8%
310d 3402
 
0.8%
Other values (4987) 365944
89.6%
2024-07-25T13:59:31.617585image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 227021
 
12.1%
5 147110
 
7.8%
1 132422
 
7.0%
2 120766
 
6.4%
3 117927
 
6.3%
4 99339
 
5.3%
6 95130
 
5.1%
C 94397
 
5.0%
L 91907
 
4.9%
D 85066
 
4.5%
Other values (48) 671711
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1882796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 227021
 
12.1%
5 147110
 
7.8%
1 132422
 
7.0%
2 120766
 
6.4%
3 117927
 
6.3%
4 99339
 
5.3%
6 95130
 
5.1%
C 94397
 
5.0%
L 91907
 
4.9%
D 85066
 
4.5%
Other values (48) 671711
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1882796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 227021
 
12.1%
5 147110
 
7.8%
1 132422
 
7.0%
2 120766
 
6.4%
3 117927
 
6.3%
4 99339
 
5.3%
6 95130
 
5.1%
C 94397
 
5.0%
L 91907
 
4.9%
D 85066
 
4.5%
Other values (48) 671711
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1882796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 227021
 
12.1%
5 147110
 
7.8%
1 132422
 
7.0%
2 120766
 
6.4%
3 117927
 
6.3%
4 99339
 
5.3%
6 95130
 
5.1%
C 94397
 
5.0%
L 91907
 
4.9%
D 85066
 
4.5%
Other values (48) 671711
35.7%
Distinct1950
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size23.0 MiB
2024-07-25T13:59:32.116846image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length3
Mean length3.2155039
Min length1

Characters and Unicode

Total characters1289819
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique228 ?
Unique (%)0.1%

Sample

1st row521
2nd row950
3rd row226
4th rowPC120
5th rowS175
ValueCountFrequency (%)
580 19798
 
4.9%
310 17354
 
4.3%
d6 13110
 
3.3%
416 12687
 
3.2%
d5 9342
 
2.3%
950 7406
 
1.8%
d3 6826
 
1.7%
d8 6733
 
1.7%
d4 6464
 
1.6%
12 6143
 
1.5%
Other values (1911) 295729
73.6%
2024-07-25T13:59:32.805340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 226368
17.6%
5 140930
10.9%
1 123815
9.6%
2 113051
8.8%
3 111555
8.6%
4 99233
7.7%
6 87934
 
6.8%
8 69942
 
5.4%
D 65181
 
5.1%
9 47774
 
3.7%
Other values (29) 204036
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1289819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 226368
17.6%
5 140930
10.9%
1 123815
9.6%
2 113051
8.8%
3 111555
8.6%
4 99233
7.7%
6 87934
 
6.8%
8 69942
 
5.4%
D 65181
 
5.1%
9 47774
 
3.7%
Other values (29) 204036
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1289819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 226368
17.6%
5 140930
10.9%
1 123815
9.6%
2 113051
8.8%
3 111555
8.6%
4 99233
7.7%
6 87934
 
6.8%
8 69942
 
5.4%
D 65181
 
5.1%
9 47774
 
3.7%
Other values (29) 204036
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1289819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 226368
17.6%
5 140930
10.9%
1 123815
9.6%
2 113051
8.8%
3 111555
8.6%
4 99233
7.7%
6 87934
 
6.8%
8 69942
 
5.4%
D 65181
 
5.1%
9 47774
 
3.7%
Other values (29) 204036
15.8%

fiSecondaryDesc
Text

MISSING 

Distinct175
Distinct (%)0.1%
Missing137191
Missing (%)34.2%
Memory size18.8 MiB
2024-07-25T13:59:33.179608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length1
Mean length1.2446483
Min length1

Characters and Unicode

Total characters328505
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowD
2nd rowF
3rd rowG
4th rowE
5th rowD
ValueCountFrequency (%)
c 43245
16.0%
b 39251
14.5%
g 36433
13.5%
h 24051
8.9%
e 21325
7.9%
d 19457
 
7.2%
l 9361
 
3.5%
f 9153
 
3.4%
k 8682
 
3.2%
m 6818
 
2.5%
Other values (149) 52886
19.5%
2024-07-25T13:59:33.761819image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 47674
14.5%
B 39864
12.1%
G 38308
11.7%
E 32939
10.0%
H 24416
 
7.4%
D 19702
 
6.0%
L 16966
 
5.2%
R 12812
 
3.9%
P 12748
 
3.9%
S 12256
 
3.7%
Other values (27) 70820
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 328505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 47674
14.5%
B 39864
12.1%
G 38308
11.7%
E 32939
10.0%
H 24416
 
7.4%
D 19702
 
6.0%
L 16966
 
5.2%
R 12812
 
3.9%
P 12748
 
3.9%
S 12256
 
3.7%
Other values (27) 70820
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 328505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 47674
14.5%
B 39864
12.1%
G 38308
11.7%
E 32939
10.0%
H 24416
 
7.4%
D 19702
 
6.0%
L 16966
 
5.2%
R 12812
 
3.9%
P 12748
 
3.9%
S 12256
 
3.7%
Other values (27) 70820
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 328505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 47674
14.5%
B 39864
12.1%
G 38308
11.7%
E 32939
10.0%
H 24416
 
7.4%
D 19702
 
6.0%
L 16966
 
5.2%
R 12812
 
3.9%
P 12748
 
3.9%
S 12256
 
3.7%
Other values (27) 70820
21.6%

fiModelSeries
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing344217
Missing (%)85.8%
Memory size13.7 MiB

fiModelDescriptor
Text

MISSING 

Distinct139
Distinct (%)0.2%
Missing329206
Missing (%)82.1%
Memory size14.1 MiB
2024-07-25T13:59:34.239163image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length14
Median length10
Mean length1.8840362
Min length1

Characters and Unicode

Total characters135498
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowLC
2nd row6
3rd rowL
4th rowLT
5th rowCR
ValueCountFrequency (%)
l 15875
22.1%
lgp 15566
21.6%
lc 12750
17.7%
xl 6434
8.9%
6 2885
 
4.0%
lt 2357
 
3.3%
5 2246
 
3.1%
3 1887
 
2.6%
cr 1684
 
2.3%
h 1049
 
1.5%
Other values (126) 9193
12.8%
2024-07-25T13:59:34.825137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 54761
40.4%
P 15780
 
11.6%
G 15657
 
11.6%
C 15493
 
11.4%
X 7310
 
5.4%
T 4100
 
3.0%
R 3772
 
2.8%
6 2887
 
2.1%
S 2718
 
2.0%
5 2256
 
1.7%
Other values (40) 10764
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 54761
40.4%
P 15780
 
11.6%
G 15657
 
11.6%
C 15493
 
11.4%
X 7310
 
5.4%
T 4100
 
3.0%
R 3772
 
2.8%
6 2887
 
2.1%
S 2718
 
2.0%
5 2256
 
1.7%
Other values (40) 10764
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 54761
40.4%
P 15780
 
11.6%
G 15657
 
11.6%
C 15493
 
11.4%
X 7310
 
5.4%
T 4100
 
3.0%
R 3772
 
2.8%
6 2887
 
2.1%
S 2718
 
2.0%
5 2256
 
1.7%
Other values (40) 10764
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 54761
40.4%
P 15780
 
11.6%
G 15657
 
11.6%
C 15493
 
11.4%
X 7310
 
5.4%
T 4100
 
3.0%
R 3772
 
2.8%
6 2887
 
2.1%
S 2718
 
2.0%
5 2256
 
1.7%
Other values (40) 10764
 
7.9%

ProductSize
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing210775
Missing (%)52.5%
Memory size23.0 MiB
Medium
62274 
Large / Medium
49678 
Small
26494 
Mini
24840 
Large
20975 

Length

Max length14
Median length7
Mean length7.6094773
Min length4

Characters and Unicode

Total characters1448464
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowSmall
3rd rowLarge / Medium
4th rowMini
5th rowLarge

Common Values

ValueCountFrequency (%)
Medium 62274
 
15.5%
Large / Medium 49678
 
12.4%
Small 26494
 
6.6%
Mini 24840
 
6.2%
Large 20975
 
5.2%
Compact 6089
 
1.5%
(Missing) 210775
52.5%

Length

2024-07-25T13:59:35.044938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:35.247964image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
medium 111952
38.6%
large 70653
24.4%
49678
17.1%
small 26494
 
9.1%
mini 24840
 
8.6%
compact 6089
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 182605
12.6%
i 161632
11.2%
m 144535
10.0%
M 136792
9.4%
d 111952
7.7%
u 111952
7.7%
a 103236
7.1%
99356
 
6.9%
r 70653
 
4.9%
g 70653
 
4.9%
Other values (10) 255098
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1448464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 182605
12.6%
i 161632
11.2%
m 144535
10.0%
M 136792
9.4%
d 111952
7.7%
u 111952
7.7%
a 103236
7.1%
99356
 
6.9%
r 70653
 
4.9%
g 70653
 
4.9%
Other values (10) 255098
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1448464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 182605
12.6%
i 161632
11.2%
m 144535
10.0%
M 136792
9.4%
d 111952
7.7%
u 111952
7.7%
a 103236
7.1%
99356
 
6.9%
r 70653
 
4.9%
g 70653
 
4.9%
Other values (10) 255098
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1448464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 182605
12.6%
i 161632
11.2%
m 144535
10.0%
M 136792
9.4%
d 111952
7.7%
u 111952
7.7%
a 103236
7.1%
99356
 
6.9%
r 70653
 
4.9%
g 70653
 
4.9%
Other values (10) 255098
17.6%
Distinct74
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.8 MiB
2024-07-25T13:59:35.591326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length64
Median length57
Mean length49.731961
Min length26

Characters and Unicode

Total characters19948733
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWheel Loader - 110.0 to 120.0 Horsepower
2nd rowWheel Loader - 150.0 to 175.0 Horsepower
3rd rowSkid Steer Loader - 1351.0 to 1601.0 Lb Operating Capacity
4th rowHydraulic Excavator, Track - 12.0 to 14.0 Metric Tons
5th rowSkid Steer Loader - 1601.0 to 1751.0 Lb Operating Capacity
ValueCountFrequency (%)
418488
 
12.1%
to 376240
 
10.9%
loader 193949
 
5.6%
track 181687
 
5.2%
horsepower 176503
 
5.1%
excavator 101167
 
2.9%
hydraulic 101167
 
2.9%
tons 101012
 
2.9%
metric 101012
 
2.9%
type 80520
 
2.3%
Other values (73) 1633442
47.1%
2024-07-25T13:59:36.173803image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3064062
15.4%
r 1512237
 
7.6%
o 1416921
 
7.1%
e 1271503
 
6.4%
a 1144740
 
5.7%
0 1096732
 
5.5%
t 1046283
 
5.2%
. 772451
 
3.9%
c 687793
 
3.4%
1 568797
 
2.9%
Other values (44) 7367214
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19948733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3064062
15.4%
r 1512237
 
7.6%
o 1416921
 
7.1%
e 1271503
 
6.4%
a 1144740
 
5.7%
0 1096732
 
5.5%
t 1046283
 
5.2%
. 772451
 
3.9%
c 687793
 
3.4%
1 568797
 
2.9%
Other values (44) 7367214
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19948733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3064062
15.4%
r 1512237
 
7.6%
o 1416921
 
7.1%
e 1271503
 
6.4%
a 1144740
 
5.7%
0 1096732
 
5.5%
t 1046283
 
5.2%
. 772451
 
3.9%
c 687793
 
3.4%
1 568797
 
2.9%
Other values (44) 7367214
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19948733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3064062
15.4%
r 1512237
 
7.6%
o 1416921
 
7.1%
e 1271503
 
6.4%
a 1144740
 
5.7%
0 1096732
 
5.5%
t 1046283
 
5.2%
. 772451
 
3.9%
c 687793
 
3.4%
1 568797
 
2.9%
Other values (44) 7367214
36.9%

state
Text

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.9 MiB
2024-07-25T13:59:36.463883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.0858236
Min length4

Characters and Unicode

Total characters3243426
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowNorth Carolina
3rd rowNew York
4th rowTexas
5th rowNew York
ValueCountFrequency (%)
florida 63944
 
14.3%
texas 51682
 
11.5%
california 29019
 
6.5%
new 25739
 
5.7%
carolina 20198
 
4.5%
washington 15957
 
3.6%
georgia 14309
 
3.2%
maryland 12965
 
2.9%
mississippi 12961
 
2.9%
ohio 12190
 
2.7%
Other values (46) 189577
42.3%
2024-07-25T13:59:36.985314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 421346
13.0%
i 361630
 
11.1%
o 285021
 
8.8%
n 245009
 
7.6%
s 222884
 
6.9%
e 213333
 
6.6%
r 211092
 
6.5%
l 182068
 
5.6%
d 103925
 
3.2%
t 72609
 
2.2%
Other values (36) 924509
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3243426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 421346
13.0%
i 361630
 
11.1%
o 285021
 
8.8%
n 245009
 
7.6%
s 222884
 
6.9%
e 213333
 
6.6%
r 211092
 
6.5%
l 182068
 
5.6%
d 103925
 
3.2%
t 72609
 
2.2%
Other values (36) 924509
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3243426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 421346
13.0%
i 361630
 
11.1%
o 285021
 
8.8%
n 245009
 
7.6%
s 222884
 
6.9%
e 213333
 
6.6%
r 211092
 
6.5%
l 182068
 
5.6%
d 103925
 
3.2%
t 72609
 
2.2%
Other values (36) 924509
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3243426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 421346
13.0%
i 361630
 
11.1%
o 285021
 
8.8%
n 245009
 
7.6%
s 222884
 
6.9%
e 213333
 
6.6%
r 211092
 
6.5%
l 182068
 
5.6%
d 103925
 
3.2%
t 72609
 
2.2%
Other values (36) 924509
28.5%

ProductGroup
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
TEX
101167 
TTT
80520 
BL
79415 
WL
71046 
SSL
43488 

Length

Max length3
Median length3
Mean length2.5613587
Min length2

Characters and Unicode

Total characters1027425
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWL
2nd rowWL
3rd rowSSL
4th rowTEX
5th rowSSL

Common Values

ValueCountFrequency (%)
TEX 101167
25.2%
TTT 80520
20.1%
BL 79415
19.8%
WL 71046
17.7%
SSL 43488
10.8%
MG 25489
 
6.4%

Length

2024-07-25T13:59:37.207573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:37.399093image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
tex 101167
25.2%
ttt 80520
20.1%
bl 79415
19.8%
wl 71046
17.7%
ssl 43488
10.8%
mg 25489
 
6.4%

Most occurring characters

ValueCountFrequency (%)
T 342727
33.4%
L 193949
18.9%
E 101167
 
9.8%
X 101167
 
9.8%
S 86976
 
8.5%
B 79415
 
7.7%
W 71046
 
6.9%
M 25489
 
2.5%
G 25489
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1027425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 342727
33.4%
L 193949
18.9%
E 101167
 
9.8%
X 101167
 
9.8%
S 86976
 
8.5%
B 79415
 
7.7%
W 71046
 
6.9%
M 25489
 
2.5%
G 25489
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1027425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 342727
33.4%
L 193949
18.9%
E 101167
 
9.8%
X 101167
 
9.8%
S 86976
 
8.5%
B 79415
 
7.7%
W 71046
 
6.9%
M 25489
 
2.5%
G 25489
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1027425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 342727
33.4%
L 193949
18.9%
E 101167
 
9.8%
X 101167
 
9.8%
S 86976
 
8.5%
B 79415
 
7.7%
W 71046
 
6.9%
M 25489
 
2.5%
G 25489
 
2.5%

ProductGroupDesc
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.8 MiB
Track Excavators
101167 
Track Type Tractors
80520 
Backhoe Loaders
79415 
Wheel Loader
71046 
Skid Steer Loaders
43488 

Length

Max length19
Median length16
Mean length15.721957
Min length12

Characters and Unicode

Total characters6306470
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWheel Loader
2nd rowWheel Loader
3rd rowSkid Steer Loaders
4th rowTrack Excavators
5th rowSkid Steer Loaders

Common Values

ValueCountFrequency (%)
Track Excavators 101167
25.2%
Track Type Tractors 80520
20.1%
Backhoe Loaders 79415
19.8%
Wheel Loader 71046
17.7%
Skid Steer Loaders 43488
10.8%
Motor Graders 25489
 
6.4%

Length

2024-07-25T13:59:37.636941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:37.857447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
track 181687
19.6%
loaders 122903
13.3%
excavators 101167
10.9%
type 80520
8.7%
tractors 80520
8.7%
backhoe 79415
8.6%
wheel 71046
 
7.7%
loader 71046
 
7.7%
skid 43488
 
4.7%
steer 43488
 
4.7%
Other values (2) 50978
 
5.5%

Most occurring characters

ValueCountFrequency (%)
a 763394
12.1%
r 757798
12.0%
e 608441
9.6%
525133
 
8.3%
o 506029
 
8.0%
c 442789
 
7.0%
T 342727
 
5.4%
s 330079
 
5.2%
k 304590
 
4.8%
d 262926
 
4.2%
Other values (15) 1462564
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6306470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 763394
12.1%
r 757798
12.0%
e 608441
9.6%
525133
 
8.3%
o 506029
 
8.0%
c 442789
 
7.0%
T 342727
 
5.4%
s 330079
 
5.2%
k 304590
 
4.8%
d 262926
 
4.2%
Other values (15) 1462564
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6306470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 763394
12.1%
r 757798
12.0%
e 608441
9.6%
525133
 
8.3%
o 506029
 
8.0%
c 442789
 
7.0%
T 342727
 
5.4%
s 330079
 
5.2%
k 304590
 
4.8%
d 262926
 
4.2%
Other values (15) 1462564
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6306470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 763394
12.1%
r 757798
12.0%
e 608441
9.6%
525133
 
8.3%
o 506029
 
8.0%
c 442789
 
7.0%
T 342727
 
5.4%
s 330079
 
5.2%
k 304590
 
4.8%
d 262926
 
4.2%
Other values (15) 1462564
23.2%

Drive_System
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing296764
Missing (%)74.0%
Memory size22.7 MiB
Two Wheel Drive
46139 
Four Wheel Drive
32996 
No
24428 
All Wheel Drive
 
798

Length

Max length16
Median length15
Mean length12.273234
Min length2

Characters and Unicode

Total characters1280847
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFour Wheel Drive
2nd rowFour Wheel Drive
3rd rowFour Wheel Drive
4th rowFour Wheel Drive
5th rowFour Wheel Drive

Common Values

ValueCountFrequency (%)
Two Wheel Drive 46139
 
11.5%
Four Wheel Drive 32996
 
8.2%
No 24428
 
6.1%
All Wheel Drive 798
 
0.2%
(Missing) 296764
74.0%

Length

2024-07-25T13:59:38.186047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:38.421522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
wheel 79933
30.3%
drive 79933
30.3%
two 46139
17.5%
four 32996
12.5%
no 24428
 
9.2%
all 798
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 239799
18.7%
159866
12.5%
r 112929
8.8%
o 103563
8.1%
l 81529
 
6.4%
W 79933
 
6.2%
h 79933
 
6.2%
D 79933
 
6.2%
i 79933
 
6.2%
v 79933
 
6.2%
Other values (6) 183496
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1280847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 239799
18.7%
159866
12.5%
r 112929
8.8%
o 103563
8.1%
l 81529
 
6.4%
W 79933
 
6.2%
h 79933
 
6.2%
D 79933
 
6.2%
i 79933
 
6.2%
v 79933
 
6.2%
Other values (6) 183496
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1280847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 239799
18.7%
159866
12.5%
r 112929
8.8%
o 103563
8.1%
l 81529
 
6.4%
W 79933
 
6.2%
h 79933
 
6.2%
D 79933
 
6.2%
i 79933
 
6.2%
v 79933
 
6.2%
Other values (6) 183496
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1280847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 239799
18.7%
159866
12.5%
r 112929
8.8%
o 103563
8.1%
l 81529
 
6.4%
W 79933
 
6.2%
h 79933
 
6.2%
D 79933
 
6.2%
i 79933
 
6.2%
v 79933
 
6.2%
Other values (6) 183496
14.3%

Enclosure
Categorical

Distinct6
Distinct (%)< 0.1%
Missing325
Missing (%)0.1%
Memory size24.1 MiB
OROPS
173932 
EROPS
139026 
EROPS w AC
87820 
EROPS AC
 
17
NO ROPS
 
3

Length

Max length19
Median length5
Mean length6.095771
Min length5

Characters and Unicode

Total characters2443185
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEROPS w AC
2nd rowEROPS w AC
3rd rowOROPS
4th rowEROPS w AC
5th rowEROPS

Common Values

ValueCountFrequency (%)
OROPS 173932
43.4%
EROPS 139026
34.7%
EROPS w AC 87820
21.9%
EROPS AC 17
 
< 0.1%
NO ROPS 3
 
< 0.1%
None or Unspecified 2
 
< 0.1%
(Missing) 325
 
0.1%

Length

2024-07-25T13:59:38.680642image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:38.893389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
erops 226863
39.4%
orops 173932
30.2%
ac 87837
 
15.2%
w 87820
 
15.2%
no 3
 
< 0.1%
rops 3
 
< 0.1%
none 2
 
< 0.1%
or 2
 
< 0.1%
unspecified 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 574733
23.5%
P 400798
16.4%
S 400798
16.4%
R 400798
16.4%
E 226863
 
9.3%
175664
 
7.2%
A 87837
 
3.6%
C 87837
 
3.6%
w 87820
 
3.6%
e 6
 
< 0.1%
Other values (11) 31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2443185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 574733
23.5%
P 400798
16.4%
S 400798
16.4%
R 400798
16.4%
E 226863
 
9.3%
175664
 
7.2%
A 87837
 
3.6%
C 87837
 
3.6%
w 87820
 
3.6%
e 6
 
< 0.1%
Other values (11) 31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2443185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 574733
23.5%
P 400798
16.4%
S 400798
16.4%
R 400798
16.4%
E 226863
 
9.3%
175664
 
7.2%
A 87837
 
3.6%
C 87837
 
3.6%
w 87820
 
3.6%
e 6
 
< 0.1%
Other values (11) 31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2443185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 574733
23.5%
P 400798
16.4%
S 400798
16.4%
R 400798
16.4%
E 226863
 
9.3%
175664
 
7.2%
A 87837
 
3.6%
C 87837
 
3.6%
w 87820
 
3.6%
e 6
 
< 0.1%
Other values (11) 31
 
< 0.1%

Forks
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing209048
Missing (%)52.1%
Memory size24.9 MiB
None or Unspecified
178300 
Yes
 
13777

Length

Max length19
Median length19
Mean length17.852377
Min length3

Characters and Unicode

Total characters3429031
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 178300
44.4%
Yes 13777
 
3.4%
(Missing) 209048
52.1%

Length

2024-07-25T13:59:39.305585image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:39.506794image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 178300
32.5%
or 178300
32.5%
unspecified 178300
32.5%
yes 13777
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e 548677
16.0%
o 356600
10.4%
n 356600
10.4%
356600
10.4%
i 356600
10.4%
s 192077
 
5.6%
N 178300
 
5.2%
r 178300
 
5.2%
U 178300
 
5.2%
p 178300
 
5.2%
Other values (4) 548677
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3429031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 548677
16.0%
o 356600
10.4%
n 356600
10.4%
356600
10.4%
i 356600
10.4%
s 192077
 
5.6%
N 178300
 
5.2%
r 178300
 
5.2%
U 178300
 
5.2%
p 178300
 
5.2%
Other values (4) 548677
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3429031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 548677
16.0%
o 356600
10.4%
n 356600
10.4%
356600
10.4%
i 356600
10.4%
s 192077
 
5.6%
N 178300
 
5.2%
r 178300
 
5.2%
U 178300
 
5.2%
p 178300
 
5.2%
Other values (4) 548677
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3429031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 548677
16.0%
o 356600
10.4%
n 356600
10.4%
356600
10.4%
i 356600
10.4%
s 192077
 
5.6%
N 178300
 
5.2%
r 178300
 
5.2%
U 178300
 
5.2%
p 178300
 
5.2%
Other values (4) 548677
16.0%

Pad_Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing321991
Missing (%)80.3%
Memory size22.8 MiB
None or Unspecified
70614 
Reversible
 
5832
Street
 
2663
Grouser
 
25

Length

Max length19
Median length19
Mean length17.895456
Min length6

Characters and Unicode

Total characters1416139
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowReversible
3rd rowStreet
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 70614
 
17.6%
Reversible 5832
 
1.5%
Street 2663
 
0.7%
Grouser 25
 
< 0.1%
(Missing) 321991
80.3%

Length

2024-07-25T13:59:39.691940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:39.901068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 70614
32.0%
or 70614
32.0%
unspecified 70614
32.0%
reversible 5832
 
2.6%
street 2663
 
1.2%
grouser 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 234689
16.6%
i 147060
10.4%
o 141253
10.0%
n 141228
10.0%
141228
10.0%
r 79159
 
5.6%
s 76471
 
5.4%
d 70614
 
5.0%
f 70614
 
5.0%
N 70614
 
5.0%
Other values (11) 243209
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1416139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 234689
16.6%
i 147060
10.4%
o 141253
10.0%
n 141228
10.0%
141228
10.0%
r 79159
 
5.6%
s 76471
 
5.4%
d 70614
 
5.0%
f 70614
 
5.0%
N 70614
 
5.0%
Other values (11) 243209
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1416139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 234689
16.6%
i 147060
10.4%
o 141253
10.0%
n 141228
10.0%
141228
10.0%
r 79159
 
5.6%
s 76471
 
5.4%
d 70614
 
5.0%
f 70614
 
5.0%
N 70614
 
5.0%
Other values (11) 243209
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1416139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 234689
16.6%
i 147060
10.4%
o 141253
10.0%
n 141228
10.0%
141228
10.0%
r 79159
 
5.6%
s 76471
 
5.4%
d 70614
 
5.0%
f 70614
 
5.0%
N 70614
 
5.0%
Other values (11) 243209
17.2%

Ride_Control
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing252519
Missing (%)63.0%
Memory size22.9 MiB
No
77685 
None or Unspecified
63116 
Yes
7805 

Length

Max length19
Median length2
Mean length9.2727683
Min length2

Characters and Unicode

Total characters1377989
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNo
4th rowNo
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
No 77685
 
19.4%
None or Unspecified 63116
 
15.7%
Yes 7805
 
1.9%
(Missing) 252519
63.0%

Length

2024-07-25T13:59:40.135146image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:40.320623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
no 77685
28.3%
none 63116
23.0%
or 63116
23.0%
unspecified 63116
23.0%
yes 7805
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 203917
14.8%
e 197153
14.3%
N 140801
10.2%
n 126232
9.2%
126232
9.2%
i 126232
9.2%
s 70921
 
5.1%
r 63116
 
4.6%
U 63116
 
4.6%
p 63116
 
4.6%
Other values (4) 197153
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1377989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 203917
14.8%
e 197153
14.3%
N 140801
10.2%
n 126232
9.2%
126232
9.2%
i 126232
9.2%
s 70921
 
5.1%
r 63116
 
4.6%
U 63116
 
4.6%
p 63116
 
4.6%
Other values (4) 197153
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1377989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 203917
14.8%
e 197153
14.3%
N 140801
10.2%
n 126232
9.2%
126232
9.2%
i 126232
9.2%
s 70921
 
5.1%
r 63116
 
4.6%
U 63116
 
4.6%
p 63116
 
4.6%
Other values (4) 197153
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1377989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 203917
14.8%
e 197153
14.3%
N 140801
10.2%
n 126232
9.2%
126232
9.2%
i 126232
9.2%
s 70921
 
5.1%
r 63116
 
4.6%
U 63116
 
4.6%
p 63116
 
4.6%
Other values (4) 197153
14.3%

Stick
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing321991
Missing (%)80.3%
Memory size22.1 MiB
Standard
48829 
Extended
30305 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters633072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExtended
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 48829
 
12.2%
Extended 30305
 
7.6%
(Missing) 321991
80.3%

Length

2024-07-25T13:59:40.522540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:40.839157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 48829
61.7%
extended 30305
38.3%

Most occurring characters

ValueCountFrequency (%)
d 158268
25.0%
a 97658
15.4%
t 79134
12.5%
n 79134
12.5%
e 60610
 
9.6%
S 48829
 
7.7%
r 48829
 
7.7%
E 30305
 
4.8%
x 30305
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 633072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 158268
25.0%
a 97658
15.4%
t 79134
12.5%
n 79134
12.5%
e 60610
 
9.6%
S 48829
 
7.7%
r 48829
 
7.7%
E 30305
 
4.8%
x 30305
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 633072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 158268
25.0%
a 97658
15.4%
t 79134
12.5%
n 79134
12.5%
e 60610
 
9.6%
S 48829
 
7.7%
r 48829
 
7.7%
E 30305
 
4.8%
x 30305
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 633072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 158268
25.0%
a 97658
15.4%
t 79134
12.5%
n 79134
12.5%
e 60610
 
9.6%
S 48829
 
7.7%
r 48829
 
7.7%
E 30305
 
4.8%
x 30305
 
4.8%

Transmission
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing217895
Missing (%)54.3%
Memory size23.3 MiB
Standard
140328 
None or Unspecified
23147 
Powershift
 
11731
Powershuttle
 
4244
Hydrostatic
 
3204
Other values (3)
 
576

Length

Max length19
Median length8
Mean length9.6727446
Min length8

Characters and Unicode

Total characters1772337
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPowershuttle
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 140328
35.0%
None or Unspecified 23147
 
5.8%
Powershift 11731
 
2.9%
Powershuttle 4244
 
1.1%
Hydrostatic 3204
 
0.8%
Direct Drive 418
 
0.1%
Autoshift 114
 
< 0.1%
AutoShift 44
 
< 0.1%
(Missing) 217895
54.3%

Length

2024-07-25T13:59:41.046247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:41.278808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 140328
61.0%
none 23147
 
10.1%
or 23147
 
10.1%
unspecified 23147
 
10.1%
powershift 11731
 
5.1%
powershuttle 4244
 
1.8%
hydrostatic 3204
 
1.4%
direct 418
 
0.2%
drive 418
 
0.2%
autoshift 158
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 307007
17.3%
a 283860
16.0%
n 186622
10.5%
r 183490
10.4%
t 167689
9.5%
S 140372
7.9%
e 90496
 
5.1%
o 65631
 
3.7%
i 62223
 
3.5%
46712
 
2.6%
Other values (16) 238235
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1772337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 307007
17.3%
a 283860
16.0%
n 186622
10.5%
r 183490
10.4%
t 167689
9.5%
S 140372
7.9%
e 90496
 
5.1%
o 65631
 
3.7%
i 62223
 
3.5%
46712
 
2.6%
Other values (16) 238235
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1772337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 307007
17.3%
a 283860
16.0%
n 186622
10.5%
r 183490
10.4%
t 167689
9.5%
S 140372
7.9%
e 90496
 
5.1%
o 65631
 
3.7%
i 62223
 
3.5%
46712
 
2.6%
Other values (16) 238235
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1772337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 307007
17.3%
a 283860
16.0%
n 186622
10.5%
r 183490
10.4%
t 167689
9.5%
S 140372
7.9%
e 90496
 
5.1%
o 65631
 
3.7%
i 62223
 
3.5%
46712
 
2.6%
Other values (16) 238235
13.4%

Turbocharged
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing321991
Missing (%)80.3%
Memory size22.9 MiB
None or Unspecified
75211 
Yes
 
3923

Length

Max length19
Median length19
Mean length18.206814
Min length3

Characters and Unicode

Total characters1440778
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowYes
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 75211
 
18.8%
Yes 3923
 
1.0%
(Missing) 321991
80.3%

Length

2024-07-25T13:59:41.557998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:41.754774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 75211
32.8%
or 75211
32.8%
unspecified 75211
32.8%
yes 3923
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 229556
15.9%
o 150422
10.4%
n 150422
10.4%
150422
10.4%
i 150422
10.4%
s 79134
 
5.5%
N 75211
 
5.2%
r 75211
 
5.2%
U 75211
 
5.2%
p 75211
 
5.2%
Other values (4) 229556
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1440778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 229556
15.9%
o 150422
10.4%
n 150422
10.4%
150422
10.4%
i 150422
10.4%
s 79134
 
5.5%
N 75211
 
5.2%
r 75211
 
5.2%
U 75211
 
5.2%
p 75211
 
5.2%
Other values (4) 229556
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1440778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 229556
15.9%
o 150422
10.4%
n 150422
10.4%
150422
10.4%
i 150422
10.4%
s 79134
 
5.5%
N 75211
 
5.2%
r 75211
 
5.2%
U 75211
 
5.2%
p 75211
 
5.2%
Other values (4) 229556
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1440778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 229556
15.9%
o 150422
10.4%
n 150422
10.4%
150422
10.4%
i 150422
10.4%
s 79134
 
5.5%
N 75211
 
5.2%
r 75211
 
5.2%
U 75211
 
5.2%
p 75211
 
5.2%
Other values (4) 229556
15.9%

Blade_Extension
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.9 MiB
None or Unspecified
24692 
Yes
 
527

Length

Max length19
Median length19
Mean length18.665649
Min length3

Characters and Unicode

Total characters470729
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 24692
 
6.2%
Yes 527
 
0.1%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:42.011839image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:42.232932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 24692
33.1%
or 24692
33.1%
unspecified 24692
33.1%
yes 527
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 74603
15.8%
o 49384
10.5%
n 49384
10.5%
49384
10.5%
i 49384
10.5%
s 25219
 
5.4%
N 24692
 
5.2%
r 24692
 
5.2%
U 24692
 
5.2%
p 24692
 
5.2%
Other values (4) 74603
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 470729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 74603
15.8%
o 49384
10.5%
n 49384
10.5%
49384
10.5%
i 49384
10.5%
s 25219
 
5.4%
N 24692
 
5.2%
r 24692
 
5.2%
U 24692
 
5.2%
p 24692
 
5.2%
Other values (4) 74603
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 470729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 74603
15.8%
o 49384
10.5%
n 49384
10.5%
49384
10.5%
i 49384
10.5%
s 25219
 
5.4%
N 24692
 
5.2%
r 24692
 
5.2%
U 24692
 
5.2%
p 24692
 
5.2%
Other values (4) 74603
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 470729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 74603
15.8%
o 49384
10.5%
n 49384
10.5%
49384
10.5%
i 49384
10.5%
s 25219
 
5.4%
N 24692
 
5.2%
r 24692
 
5.2%
U 24692
 
5.2%
p 24692
 
5.2%
Other values (4) 74603
15.8%

Blade_Width
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.7 MiB
14'
9615 
None or Unspecified
9283 
12'
4968 
16'
 
933
13'
 
329

Length

Max length19
Median length3
Mean length8.8931361
Min length3

Characters and Unicode

Total characters224276
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd row12'
4th row14'
5th row14'

Common Values

ValueCountFrequency (%)
14' 9615
 
2.4%
None or Unspecified 9283
 
2.3%
12' 4968
 
1.2%
16' 933
 
0.2%
13' 329
 
0.1%
<12' 91
 
< 0.1%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:42.436682image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:42.651021image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
14 9615
22.0%
none 9283
21.2%
or 9283
21.2%
unspecified 9283
21.2%
12 5059
11.6%
16 933
 
2.1%
13 329
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 27849
12.4%
o 18566
 
8.3%
n 18566
 
8.3%
18566
 
8.3%
i 18566
 
8.3%
1 15936
 
7.1%
' 15936
 
7.1%
4 9615
 
4.3%
c 9283
 
4.1%
d 9283
 
4.1%
Other values (10) 62110
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 27849
12.4%
o 18566
 
8.3%
n 18566
 
8.3%
18566
 
8.3%
i 18566
 
8.3%
1 15936
 
7.1%
' 15936
 
7.1%
4 9615
 
4.3%
c 9283
 
4.1%
d 9283
 
4.1%
Other values (10) 62110
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 27849
12.4%
o 18566
 
8.3%
n 18566
 
8.3%
18566
 
8.3%
i 18566
 
8.3%
1 15936
 
7.1%
' 15936
 
7.1%
4 9615
 
4.3%
c 9283
 
4.1%
d 9283
 
4.1%
Other values (10) 62110
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 27849
12.4%
o 18566
 
8.3%
n 18566
 
8.3%
18566
 
8.3%
i 18566
 
8.3%
1 15936
 
7.1%
' 15936
 
7.1%
4 9615
 
4.3%
c 9283
 
4.1%
d 9283
 
4.1%
Other values (10) 62110
27.7%

Enclosure_Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.9 MiB
None or Unspecified
21923 
Low Profile
2510 
High Profile
 
786

Length

Max length19
Median length19
Mean length17.985606
Min length11

Characters and Unicode

Total characters453579
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowLow Profile

Common Values

ValueCountFrequency (%)
None or Unspecified 21923
 
5.5%
Low Profile 2510
 
0.6%
High Profile 786
 
0.2%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:42.921123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:43.121040image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 21923
30.3%
or 21923
30.3%
unspecified 21923
30.3%
profile 3296
 
4.6%
low 2510
 
3.5%
high 786
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 69065
15.2%
o 49652
10.9%
i 47928
10.6%
47142
10.4%
n 43846
9.7%
r 25219
 
5.6%
f 25219
 
5.6%
c 21923
 
4.8%
d 21923
 
4.8%
N 21923
 
4.8%
Other values (10) 79739
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 69065
15.2%
o 49652
10.9%
i 47928
10.6%
47142
10.4%
n 43846
9.7%
r 25219
 
5.6%
f 25219
 
5.6%
c 21923
 
4.8%
d 21923
 
4.8%
N 21923
 
4.8%
Other values (10) 79739
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 69065
15.2%
o 49652
10.9%
i 47928
10.6%
47142
10.4%
n 43846
9.7%
r 25219
 
5.6%
f 25219
 
5.6%
c 21923
 
4.8%
d 21923
 
4.8%
N 21923
 
4.8%
Other values (10) 79739
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 69065
15.2%
o 49652
10.9%
i 47928
10.6%
47142
10.4%
n 43846
9.7%
r 25219
 
5.6%
f 25219
 
5.6%
c 21923
 
4.8%
d 21923
 
4.8%
N 21923
 
4.8%
Other values (10) 79739
17.6%

Engine_Horsepower
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.5 MiB
No
23937 
Variable
 
1282

Length

Max length8
Median length2
Mean length2.3050081
Min length2

Characters and Unicode

Total characters58130
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowVariable

Common Values

ValueCountFrequency (%)
No 23937
 
6.0%
Variable 1282
 
0.3%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:43.352974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:43.536920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
no 23937
94.9%
variable 1282
 
5.1%

Most occurring characters

ValueCountFrequency (%)
N 23937
41.2%
o 23937
41.2%
a 2564
 
4.4%
V 1282
 
2.2%
r 1282
 
2.2%
i 1282
 
2.2%
b 1282
 
2.2%
l 1282
 
2.2%
e 1282
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 23937
41.2%
o 23937
41.2%
a 2564
 
4.4%
V 1282
 
2.2%
r 1282
 
2.2%
i 1282
 
2.2%
b 1282
 
2.2%
l 1282
 
2.2%
e 1282
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 23937
41.2%
o 23937
41.2%
a 2564
 
4.4%
V 1282
 
2.2%
r 1282
 
2.2%
i 1282
 
2.2%
b 1282
 
2.2%
l 1282
 
2.2%
e 1282
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 23937
41.2%
o 23937
41.2%
a 2564
 
4.4%
V 1282
 
2.2%
r 1282
 
2.2%
i 1282
 
2.2%
b 1282
 
2.2%
l 1282
 
2.2%
e 1282
 
2.2%

Hydraulics
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing80555
Missing (%)20.1%
Memory size24.3 MiB
2 Valve
141404 
Standard
104423 
Auxiliary
40737 
Base + 1 Function
24770 
3 Valve
 
5622
Other values (7)
 
3614

Length

Max length19
Median length17
Mean length8.3730449
Min length7

Characters and Unicode

Total characters2684147
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 Valve
2nd row2 Valve
3rd rowAuxiliary
4th row2 Valve
5th rowAuxiliary

Common Values

ValueCountFrequency (%)
2 Valve 141404
35.3%
Standard 104423
26.0%
Auxiliary 40737
 
10.2%
Base + 1 Function 24770
 
6.2%
3 Valve 5622
 
1.4%
4 Valve 2960
 
0.7%
Base + 3 Function 299
 
0.1%
Base + 2 Function 127
 
< 0.1%
Base + 5 Function 89
 
< 0.1%
Base + 4 Function 76
 
< 0.1%
Other values (2) 63
 
< 0.1%
(Missing) 80555
20.1%

Length

2024-07-25T13:59:43.737754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valve 149986
27.4%
2 141531
25.9%
standard 104423
19.1%
auxiliary 40737
 
7.4%
base 25414
 
4.6%
25414
 
4.6%
function 25414
 
4.6%
1 24770
 
4.5%
3 5921
 
1.1%
4 3036
 
0.6%
Other values (5) 172
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 424983
15.8%
226248
 
8.4%
d 208856
 
7.8%
l 190723
 
7.1%
e 175430
 
6.5%
n 155271
 
5.8%
V 149986
 
5.6%
v 149986
 
5.6%
r 145170
 
5.4%
2 141531
 
5.3%
Other values (22) 715963
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2684147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 424983
15.8%
226248
 
8.4%
d 208856
 
7.8%
l 190723
 
7.1%
e 175430
 
6.5%
n 155271
 
5.8%
V 149986
 
5.6%
v 149986
 
5.6%
r 145170
 
5.4%
2 141531
 
5.3%
Other values (22) 715963
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2684147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 424983
15.8%
226248
 
8.4%
d 208856
 
7.8%
l 190723
 
7.1%
e 175430
 
6.5%
n 155271
 
5.8%
V 149986
 
5.6%
v 149986
 
5.6%
r 145170
 
5.4%
2 141531
 
5.3%
Other values (22) 715963
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2684147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 424983
15.8%
226248
 
8.4%
d 208856
 
7.8%
l 190723
 
7.1%
e 175430
 
6.5%
n 155271
 
5.8%
V 149986
 
5.6%
v 149986
 
5.6%
r 145170
 
5.4%
2 141531
 
5.3%
Other values (22) 715963
26.7%

Pushblock
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.8 MiB
None or Unspecified
19463 
Yes
5756 

Length

Max length19
Median length19
Mean length15.34815
Min length3

Characters and Unicode

Total characters387065
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowYes
4th rowNone or Unspecified
5th rowYes

Common Values

ValueCountFrequency (%)
None or Unspecified 19463
 
4.9%
Yes 5756
 
1.4%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:44.008775image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:44.200984image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 19463
30.3%
or 19463
30.3%
unspecified 19463
30.3%
yes 5756
 
9.0%

Most occurring characters

ValueCountFrequency (%)
e 64145
16.6%
o 38926
10.1%
n 38926
10.1%
38926
10.1%
i 38926
10.1%
s 25219
 
6.5%
N 19463
 
5.0%
r 19463
 
5.0%
U 19463
 
5.0%
p 19463
 
5.0%
Other values (4) 64145
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 387065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64145
16.6%
o 38926
10.1%
n 38926
10.1%
38926
10.1%
i 38926
10.1%
s 25219
 
6.5%
N 19463
 
5.0%
r 19463
 
5.0%
U 19463
 
5.0%
p 19463
 
5.0%
Other values (4) 64145
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 387065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64145
16.6%
o 38926
10.1%
n 38926
10.1%
38926
10.1%
i 38926
10.1%
s 25219
 
6.5%
N 19463
 
5.0%
r 19463
 
5.0%
U 19463
 
5.0%
p 19463
 
5.0%
Other values (4) 64145
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 387065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64145
16.6%
o 38926
10.1%
n 38926
10.1%
38926
10.1%
i 38926
10.1%
s 25219
 
6.5%
N 19463
 
5.0%
r 19463
 
5.0%
U 19463
 
5.0%
p 19463
 
5.0%
Other values (4) 64145
16.6%

Ripper
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing296988
Missing (%)74.0%
Memory size23.2 MiB
None or Unspecified
83452 
Yes
 
7902
Multi Shank
 
7633
Single Shank
 
5150

Length

Max length19
Median length19
Mean length16.853347
Min length3

Characters and Unicode

Total characters1755057
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 83452
 
20.8%
Yes 7902
 
2.0%
Multi Shank 7633
 
1.9%
Single Shank 5150
 
1.3%
(Missing) 296988
74.0%

Length

2024-07-25T13:59:44.413427image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:44.607726image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 83452
29.4%
or 83452
29.4%
unspecified 83452
29.4%
shank 12783
 
4.5%
yes 7902
 
2.8%
multi 7633
 
2.7%
single 5150
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 263408
15.0%
n 184837
10.5%
179687
10.2%
i 179687
10.2%
o 166904
9.5%
s 91354
 
5.2%
N 83452
 
4.8%
c 83452
 
4.8%
d 83452
 
4.8%
f 83452
 
4.8%
Other values (13) 355372
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1755057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 263408
15.0%
n 184837
10.5%
179687
10.2%
i 179687
10.2%
o 166904
9.5%
s 91354
 
5.2%
N 83452
 
4.8%
c 83452
 
4.8%
d 83452
 
4.8%
f 83452
 
4.8%
Other values (13) 355372
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1755057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 263408
15.0%
n 184837
10.5%
179687
10.2%
i 179687
10.2%
o 166904
9.5%
s 91354
 
5.2%
N 83452
 
4.8%
c 83452
 
4.8%
d 83452
 
4.8%
f 83452
 
4.8%
Other values (13) 355372
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1755057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 263408
15.0%
n 184837
10.5%
179687
10.2%
i 179687
10.2%
o 166904
9.5%
s 91354
 
5.2%
N 83452
 
4.8%
c 83452
 
4.8%
d 83452
 
4.8%
f 83452
 
4.8%
Other values (13) 355372
20.2%

Scarifier
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing375895
Missing (%)93.7%
Memory size21.7 MiB
None or Unspecified
12719 
Yes
12511 

Length

Max length19
Median length19
Mean length11.065953
Min length3

Characters and Unicode

Total characters279194
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 12719
 
3.2%
Yes 12511
 
3.1%
(Missing) 375895
93.7%

Length

2024-07-25T13:59:44.832199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:45.027371image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 12719
25.1%
or 12719
25.1%
unspecified 12719
25.1%
yes 12511
24.7%

Most occurring characters

ValueCountFrequency (%)
e 50668
18.1%
o 25438
9.1%
n 25438
9.1%
25438
9.1%
i 25438
9.1%
s 25230
9.0%
N 12719
 
4.6%
r 12719
 
4.6%
U 12719
 
4.6%
p 12719
 
4.6%
Other values (4) 50668
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 50668
18.1%
o 25438
9.1%
n 25438
9.1%
25438
9.1%
i 25438
9.1%
s 25230
9.0%
N 12719
 
4.6%
r 12719
 
4.6%
U 12719
 
4.6%
p 12719
 
4.6%
Other values (4) 50668
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 50668
18.1%
o 25438
9.1%
n 25438
9.1%
25438
9.1%
i 25438
9.1%
s 25230
9.0%
N 12719
 
4.6%
r 12719
 
4.6%
U 12719
 
4.6%
p 12719
 
4.6%
Other values (4) 50668
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 50668
18.1%
o 25438
9.1%
n 25438
9.1%
25438
9.1%
i 25438
9.1%
s 25230
9.0%
N 12719
 
4.6%
r 12719
 
4.6%
U 12719
 
4.6%
p 12719
 
4.6%
Other values (4) 50668
18.1%

Tip_Control
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing375906
Missing (%)93.7%
Memory size21.8 MiB
None or Unspecified
16207 
Sideshift & Tip
7070 
Tip
1942 

Length

Max length19
Median length19
Mean length16.646536
Min length3

Characters and Unicode

Total characters419809
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSideshift & Tip
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 16207
 
4.0%
Sideshift & Tip 7070
 
1.8%
Tip 1942
 
0.5%
(Missing) 375906
93.7%

Length

2024-07-25T13:59:45.231696image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:45.409629image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 16207
22.6%
or 16207
22.6%
unspecified 16207
22.6%
tip 9012
12.6%
sideshift 7070
9.9%
7070
9.9%

Most occurring characters

ValueCountFrequency (%)
e 55691
13.3%
i 55566
13.2%
46554
11.1%
n 32414
 
7.7%
o 32414
 
7.7%
p 25219
 
6.0%
d 23277
 
5.5%
f 23277
 
5.5%
s 23277
 
5.5%
N 16207
 
3.9%
Other values (8) 85913
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 419809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 55691
13.3%
i 55566
13.2%
46554
11.1%
n 32414
 
7.7%
o 32414
 
7.7%
p 25219
 
6.0%
d 23277
 
5.5%
f 23277
 
5.5%
s 23277
 
5.5%
N 16207
 
3.9%
Other values (8) 85913
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 419809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 55691
13.3%
i 55566
13.2%
46554
11.1%
n 32414
 
7.7%
o 32414
 
7.7%
p 25219
 
6.0%
d 23277
 
5.5%
f 23277
 
5.5%
s 23277
 
5.5%
N 16207
 
3.9%
Other values (8) 85913
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 419809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 55691
13.3%
i 55566
13.2%
46554
11.1%
n 32414
 
7.7%
o 32414
 
7.7%
p 25219
 
6.0%
d 23277
 
5.5%
f 23277
 
5.5%
s 23277
 
5.5%
N 16207
 
3.9%
Other values (8) 85913
20.5%

Tire_Size
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)< 0.1%
Missing306407
Missing (%)76.4%
Memory size22.5 MiB
None or Unspecified
46339 
20.5
15242 
14"
8813 
23.5
8480 
26.5
 
4482
Other values (12)
11362 

Length

Max length19
Median length7
Mean length11.272081
Min length3

Characters and Unicode

Total characters1067669
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd row23.5
3rd rowNone or Unspecified
4th row13"
5th row26.5

Common Values

ValueCountFrequency (%)
None or Unspecified 46339
 
11.6%
20.5 15242
 
3.8%
14" 8813
 
2.2%
23.5 8480
 
2.1%
26.5 4482
 
1.1%
17.5 3932
 
1.0%
29.5 2704
 
0.7%
17.5" 1793
 
0.4%
13" 766
 
0.2%
20.5" 718
 
0.2%
Other values (7) 1449
 
0.4%
(Missing) 306407
76.4%

Length

2024-07-25T13:59:45.637807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 46339
24.7%
or 46339
24.7%
unspecified 46339
24.7%
20.5 15960
 
8.5%
14 8813
 
4.7%
23.5 8782
 
4.7%
17.5 5725
 
3.1%
26.5 4482
 
2.4%
29.5 2704
 
1.4%
15.5 1064
 
0.6%
Other values (5) 852
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 139017
13.0%
n 92681
 
8.7%
92681
 
8.7%
i 92681
 
8.7%
o 92678
 
8.7%
c 46342
 
4.3%
N 46339
 
4.3%
f 46339
 
4.3%
d 46339
 
4.3%
p 46339
 
4.3%
Other values (15) 326233
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1067669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 139017
13.0%
n 92681
 
8.7%
92681
 
8.7%
i 92681
 
8.7%
o 92678
 
8.7%
c 46342
 
4.3%
N 46339
 
4.3%
f 46339
 
4.3%
d 46339
 
4.3%
p 46339
 
4.3%
Other values (15) 326233
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1067669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 139017
13.0%
n 92681
 
8.7%
92681
 
8.7%
i 92681
 
8.7%
o 92678
 
8.7%
c 46342
 
4.3%
N 46339
 
4.3%
f 46339
 
4.3%
d 46339
 
4.3%
p 46339
 
4.3%
Other values (15) 326233
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1067669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 139017
13.0%
n 92681
 
8.7%
92681
 
8.7%
i 92681
 
8.7%
o 92678
 
8.7%
c 46342
 
4.3%
N 46339
 
4.3%
f 46339
 
4.3%
d 46339
 
4.3%
p 46339
 
4.3%
Other values (15) 326233
30.6%

Coupler
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing187173
Missing (%)46.7%
Memory size25.2 MiB
None or Unspecified
184582 
Manual
23301 
Hydraulic
 
6069

Length

Max length19
Median length19
Mean length17.300539
Min length6

Characters and Unicode

Total characters3701485
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 184582
46.0%
Manual 23301
 
5.8%
Hydraulic 6069
 
1.5%
(Missing) 187173
46.7%

Length

2024-07-25T13:59:45.883828image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:46.081422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 184582
31.7%
or 184582
31.7%
unspecified 184582
31.7%
manual 23301
 
4.0%
hydraulic 6069
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 553746
15.0%
n 392465
10.6%
i 375233
10.1%
o 369164
10.0%
369164
10.0%
c 190651
 
5.2%
r 190651
 
5.2%
d 190651
 
5.2%
f 184582
 
5.0%
N 184582
 
5.0%
Other values (9) 700596
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3701485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 553746
15.0%
n 392465
10.6%
i 375233
10.1%
o 369164
10.0%
369164
10.0%
c 190651
 
5.2%
r 190651
 
5.2%
d 190651
 
5.2%
f 184582
 
5.0%
N 184582
 
5.0%
Other values (9) 700596
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3701485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 553746
15.0%
n 392465
10.6%
i 375233
10.1%
o 369164
10.0%
369164
10.0%
c 190651
 
5.2%
r 190651
 
5.2%
d 190651
 
5.2%
f 184582
 
5.0%
N 184582
 
5.0%
Other values (9) 700596
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3701485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 553746
15.0%
n 392465
10.6%
i 375233
10.1%
o 369164
10.0%
369164
10.0%
c 190651
 
5.2%
r 190651
 
5.2%
d 190651
 
5.2%
f 184582
 
5.0%
N 184582
 
5.0%
Other values (9) 700596
18.9%

Coupler_System
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing357667
Missing (%)89.2%
Memory size22.2 MiB
None or Unspecified
40430 
Yes
 
3028

Length

Max length19
Median length19
Mean length17.885176
Min length3

Characters and Unicode

Total characters777254
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 40430
 
10.1%
Yes 3028
 
0.8%
(Missing) 357667
89.2%

Length

2024-07-25T13:59:46.279430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:46.465249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 40430
32.5%
or 40430
32.5%
unspecified 40430
32.5%
yes 3028
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 124318
16.0%
o 80860
10.4%
n 80860
10.4%
80860
10.4%
i 80860
10.4%
s 43458
 
5.6%
N 40430
 
5.2%
r 40430
 
5.2%
U 40430
 
5.2%
p 40430
 
5.2%
Other values (4) 124318
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 777254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 124318
16.0%
o 80860
10.4%
n 80860
10.4%
80860
10.4%
i 80860
10.4%
s 43458
 
5.6%
N 40430
 
5.2%
r 40430
 
5.2%
U 40430
 
5.2%
p 40430
 
5.2%
Other values (4) 124318
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 777254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 124318
16.0%
o 80860
10.4%
n 80860
10.4%
80860
10.4%
i 80860
10.4%
s 43458
 
5.6%
N 40430
 
5.2%
r 40430
 
5.2%
U 40430
 
5.2%
p 40430
 
5.2%
Other values (4) 124318
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 777254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 124318
16.0%
o 80860
10.4%
n 80860
10.4%
80860
10.4%
i 80860
10.4%
s 43458
 
5.6%
N 40430
 
5.2%
r 40430
 
5.2%
U 40430
 
5.2%
p 40430
 
5.2%
Other values (4) 124318
16.0%

Grouser_Tracks
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing357763
Missing (%)89.2%
Memory size22.2 MiB
None or Unspecified
40515 
Yes
 
2847

Length

Max length19
Median length19
Mean length17.949495
Min length3

Characters and Unicode

Total characters778326
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
None or Unspecified 40515
 
10.1%
Yes 2847
 
0.7%
(Missing) 357763
89.2%

Length

2024-07-25T13:59:46.758017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:46.956827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 40515
32.6%
or 40515
32.6%
unspecified 40515
32.6%
yes 2847
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 124392
16.0%
o 81030
10.4%
n 81030
10.4%
81030
10.4%
i 81030
10.4%
s 43362
 
5.6%
N 40515
 
5.2%
r 40515
 
5.2%
U 40515
 
5.2%
p 40515
 
5.2%
Other values (4) 124392
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 778326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 124392
16.0%
o 81030
10.4%
n 81030
10.4%
81030
10.4%
i 81030
10.4%
s 43362
 
5.6%
N 40515
 
5.2%
r 40515
 
5.2%
U 40515
 
5.2%
p 40515
 
5.2%
Other values (4) 124392
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 778326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 124392
16.0%
o 81030
10.4%
n 81030
10.4%
81030
10.4%
i 81030
10.4%
s 43362
 
5.6%
N 40515
 
5.2%
r 40515
 
5.2%
U 40515
 
5.2%
p 40515
 
5.2%
Other values (4) 124392
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 778326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 124392
16.0%
o 81030
10.4%
n 81030
10.4%
81030
10.4%
i 81030
10.4%
s 43362
 
5.6%
N 40515
 
5.2%
r 40515
 
5.2%
U 40515
 
5.2%
p 40515
 
5.2%
Other values (4) 124392
16.0%

Hydraulics_Flow
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing357763
Missing (%)89.2%
Memory size21.8 MiB
Standard
42784 
High Flow
 
553
None or Unspecified
 
25

Length

Max length19
Median length8
Mean length8.0190951
Min length8

Characters and Unicode

Total characters347724
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 42784
 
10.7%
High Flow 553
 
0.1%
None or Unspecified 25
 
< 0.1%
(Missing) 357763
89.2%

Length

2024-07-25T13:59:47.144190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:47.306184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 42784
97.3%
high 553
 
1.3%
flow 553
 
1.3%
none 25
 
0.1%
or 25
 
0.1%
unspecified 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 85593
24.6%
a 85568
24.6%
n 42834
12.3%
r 42809
12.3%
S 42784
12.3%
t 42784
12.3%
i 603
 
0.2%
o 603
 
0.2%
603
 
0.2%
w 553
 
0.2%
Other values (12) 2990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 347724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 85593
24.6%
a 85568
24.6%
n 42834
12.3%
r 42809
12.3%
S 42784
12.3%
t 42784
12.3%
i 603
 
0.2%
o 603
 
0.2%
603
 
0.2%
w 553
 
0.2%
Other values (12) 2990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 347724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 85593
24.6%
a 85568
24.6%
n 42834
12.3%
r 42809
12.3%
S 42784
12.3%
t 42784
12.3%
i 603
 
0.2%
o 603
 
0.2%
603
 
0.2%
w 553
 
0.2%
Other values (12) 2990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 347724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 85593
24.6%
a 85568
24.6%
n 42834
12.3%
r 42809
12.3%
S 42784
12.3%
t 42784
12.3%
i 603
 
0.2%
o 603
 
0.2%
603
 
0.2%
w 553
 
0.2%
Other values (12) 2990
 
0.9%

Track_Type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing301972
Missing (%)75.3%
Memory size22.0 MiB
Steel
84880 
Rubber
14273 

Length

Max length6
Median length5
Mean length5.1439493
Min length5

Characters and Unicode

Total characters510038
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSteel
2nd rowRubber
3rd rowSteel
4th rowRubber
5th rowSteel

Common Values

ValueCountFrequency (%)
Steel 84880
 
21.2%
Rubber 14273
 
3.6%
(Missing) 301972
75.3%

Length

2024-07-25T13:59:47.475579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:47.620239image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
steel 84880
85.6%
rubber 14273
 
14.4%

Most occurring characters

ValueCountFrequency (%)
e 184033
36.1%
S 84880
16.6%
t 84880
16.6%
l 84880
16.6%
b 28546
 
5.6%
R 14273
 
2.8%
u 14273
 
2.8%
r 14273
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 184033
36.1%
S 84880
16.6%
t 84880
16.6%
l 84880
16.6%
b 28546
 
5.6%
R 14273
 
2.8%
u 14273
 
2.8%
r 14273
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 184033
36.1%
S 84880
16.6%
t 84880
16.6%
l 84880
16.6%
b 28546
 
5.6%
R 14273
 
2.8%
u 14273
 
2.8%
r 14273
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 184033
36.1%
S 84880
16.6%
t 84880
16.6%
l 84880
16.6%
b 28546
 
5.6%
R 14273
 
2.8%
u 14273
 
2.8%
r 14273
 
2.8%

Undercarriage_Pad_Width
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct19
Distinct (%)< 0.1%
Missing301253
Missing (%)75.1%
Memory size23.1 MiB
None or Unspecified
79651 
32 inch
 
5213
28 inch
 
3114
24 inch
 
2962
20 inch
 
2652
Other values (14)
 
6280

Length

Max length19
Median length19
Mean length16.57041
Min length7

Characters and Unicode

Total characters1654920
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th row16 inch

Common Values

ValueCountFrequency (%)
None or Unspecified 79651
 
19.9%
32 inch 5213
 
1.3%
28 inch 3114
 
0.8%
24 inch 2962
 
0.7%
20 inch 2652
 
0.7%
30 inch 1592
 
0.4%
36 inch 1519
 
0.4%
18 inch 1429
 
0.4%
34 inch 531
 
0.1%
16 inch 460
 
0.1%
Other values (9) 749
 
0.2%
(Missing) 301253
75.1%

Length

2024-07-25T13:59:47.775880image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 79651
28.5%
or 79651
28.5%
unspecified 79651
28.5%
inch 20221
 
7.2%
32 5213
 
1.9%
28 3114
 
1.1%
24 2962
 
1.1%
20 2652
 
0.9%
30 1592
 
0.6%
36 1519
 
0.5%
Other values (12) 3169
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 238953
14.4%
n 179523
10.8%
179523
10.8%
i 179523
10.8%
o 159302
9.6%
c 99872
 
6.0%
N 79651
 
4.8%
f 79651
 
4.8%
d 79651
 
4.8%
p 79651
 
4.8%
Other values (14) 299620
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1654920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 238953
14.4%
n 179523
10.8%
179523
10.8%
i 179523
10.8%
o 159302
9.6%
c 99872
 
6.0%
N 79651
 
4.8%
f 79651
 
4.8%
d 79651
 
4.8%
p 79651
 
4.8%
Other values (14) 299620
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1654920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 238953
14.4%
n 179523
10.8%
179523
10.8%
i 179523
10.8%
o 159302
9.6%
c 99872
 
6.0%
N 79651
 
4.8%
f 79651
 
4.8%
d 79651
 
4.8%
p 79651
 
4.8%
Other values (14) 299620
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1654920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 238953
14.4%
n 179523
10.8%
179523
10.8%
i 179523
10.8%
o 159302
9.6%
c 99872
 
6.0%
N 79651
 
4.8%
f 79651
 
4.8%
d 79651
 
4.8%
p 79651
 
4.8%
Other values (14) 299620
18.1%

Stick_Length
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct29
Distinct (%)< 0.1%
Missing301907
Missing (%)75.3%
Memory size23.1 MiB
None or Unspecified
78820 
9' 6"
 
5765
10' 6"
 
3456
11' 0"
 
1577
9' 10"
 
1439
Other values (24)
8161 

Length

Max length19
Median length19
Mean length16.23912
Min length5

Characters and Unicode

Total characters1611213
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd row11' 0"
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 78820
 
19.6%
9' 6" 5765
 
1.4%
10' 6" 3456
 
0.9%
11' 0" 1577
 
0.4%
9' 10" 1439
 
0.4%
9' 8" 1434
 
0.4%
9' 7" 1401
 
0.3%
12' 10" 1066
 
0.3%
10' 2" 983
 
0.2%
8' 6" 901
 
0.2%
Other values (19) 2376
 
0.6%
(Missing) 301907
75.3%

Length

2024-07-25T13:59:47.978078image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 78820
28.4%
or 78820
28.4%
unspecified 78820
28.4%
9 10232
 
3.7%
6 10172
 
3.7%
10 8163
 
2.9%
8 3637
 
1.3%
11 1876
 
0.7%
2 1590
 
0.6%
0 1577
 
0.6%
Other values (11) 3549
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 236460
14.7%
178038
11.0%
n 157640
9.8%
o 157640
9.8%
i 157640
9.8%
N 78820
 
4.9%
c 78820
 
4.9%
f 78820
 
4.9%
d 78820
 
4.9%
p 78820
 
4.9%
Other values (15) 329695
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1611213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 236460
14.7%
178038
11.0%
n 157640
9.8%
o 157640
9.8%
i 157640
9.8%
N 78820
 
4.9%
c 78820
 
4.9%
f 78820
 
4.9%
d 78820
 
4.9%
p 78820
 
4.9%
Other values (15) 329695
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1611213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 236460
14.7%
178038
11.0%
n 157640
9.8%
o 157640
9.8%
i 157640
9.8%
N 78820
 
4.9%
c 78820
 
4.9%
f 78820
 
4.9%
d 78820
 
4.9%
p 78820
 
4.9%
Other values (15) 329695
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1611213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 236460
14.7%
178038
11.0%
n 157640
9.8%
o 157640
9.8%
i 157640
9.8%
N 78820
 
4.9%
c 78820
 
4.9%
f 78820
 
4.9%
d 78820
 
4.9%
p 78820
 
4.9%
Other values (15) 329695
20.5%

Thumb
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing301837
Missing (%)75.2%
Memory size23.1 MiB
None or Unspecified
83093 
Manual
9358 
Hydraulic
 
6837

Length

Max length19
Median length19
Mean length17.086133
Min length6

Characters and Unicode

Total characters1696448
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 83093
 
20.7%
Manual 9358
 
2.3%
Hydraulic 6837
 
1.7%
(Missing) 301837
75.2%

Length

2024-07-25T13:59:48.360017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:48.524336image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 83093
31.3%
or 83093
31.3%
unspecified 83093
31.3%
manual 9358
 
3.5%
hydraulic 6837
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 249279
14.7%
n 175544
10.3%
i 173023
10.2%
o 166186
9.8%
166186
9.8%
c 89930
 
5.3%
r 89930
 
5.3%
d 89930
 
5.3%
f 83093
 
4.9%
N 83093
 
4.9%
Other values (9) 330254
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1696448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 249279
14.7%
n 175544
10.3%
i 173023
10.2%
o 166186
9.8%
166186
9.8%
c 89930
 
5.3%
r 89930
 
5.3%
d 89930
 
5.3%
f 83093
 
4.9%
N 83093
 
4.9%
Other values (9) 330254
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1696448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 249279
14.7%
n 175544
10.3%
i 173023
10.2%
o 166186
9.8%
166186
9.8%
c 89930
 
5.3%
r 89930
 
5.3%
d 89930
 
5.3%
f 83093
 
4.9%
N 83093
 
4.9%
Other values (9) 330254
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1696448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 249279
14.7%
n 175544
10.3%
i 173023
10.2%
o 166186
9.8%
166186
9.8%
c 89930
 
5.3%
r 89930
 
5.3%
d 89930
 
5.3%
f 83093
 
4.9%
N 83093
 
4.9%
Other values (9) 330254
19.5%

Pattern_Changer
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing301907
Missing (%)75.3%
Memory size23.2 MiB
None or Unspecified
90255 
Yes
 
8898
No
 
65

Length

Max length19
Median length19
Mean length17.553962
Min length2

Characters and Unicode

Total characters1741669
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 90255
 
22.5%
Yes 8898
 
2.2%
No 65
 
< 0.1%
(Missing) 301907
75.3%

Length

2024-07-25T13:59:48.767428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:49.017674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 90255
32.3%
or 90255
32.3%
unspecified 90255
32.3%
yes 8898
 
3.2%
no 65
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 279663
16.1%
o 180575
10.4%
n 180510
10.4%
180510
10.4%
i 180510
10.4%
s 99153
 
5.7%
N 90320
 
5.2%
r 90255
 
5.2%
U 90255
 
5.2%
p 90255
 
5.2%
Other values (4) 279663
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1741669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 279663
16.1%
o 180575
10.4%
n 180510
10.4%
180510
10.4%
i 180510
10.4%
s 99153
 
5.7%
N 90320
 
5.2%
r 90255
 
5.2%
U 90255
 
5.2%
p 90255
 
5.2%
Other values (4) 279663
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1741669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 279663
16.1%
o 180575
10.4%
n 180510
10.4%
180510
10.4%
i 180510
10.4%
s 99153
 
5.7%
N 90320
 
5.2%
r 90255
 
5.2%
U 90255
 
5.2%
p 90255
 
5.2%
Other values (4) 279663
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1741669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 279663
16.1%
o 180575
10.4%
n 180510
10.4%
180510
10.4%
i 180510
10.4%
s 99153
 
5.7%
N 90320
 
5.2%
r 90255
 
5.2%
U 90255
 
5.2%
p 90255
 
5.2%
Other values (4) 279663
16.1%

Grouser_Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing301972
Missing (%)75.3%
Memory size22.1 MiB
Double
84653 
Triple
14498 
Single
 
2

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters594918
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDouble
2nd rowDouble
3rd rowDouble
4th rowDouble
5th rowDouble

Common Values

ValueCountFrequency (%)
Double 84653
 
21.1%
Triple 14498
 
3.6%
Single 2
 
< 0.1%
(Missing) 301972
75.3%

Length

2024-07-25T13:59:49.192921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:49.333960image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
double 84653
85.4%
triple 14498
 
14.6%
single 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 99153
16.7%
e 99153
16.7%
D 84653
14.2%
o 84653
14.2%
u 84653
14.2%
b 84653
14.2%
i 14500
 
2.4%
T 14498
 
2.4%
r 14498
 
2.4%
p 14498
 
2.4%
Other values (3) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 99153
16.7%
e 99153
16.7%
D 84653
14.2%
o 84653
14.2%
u 84653
14.2%
b 84653
14.2%
i 14500
 
2.4%
T 14498
 
2.4%
r 14498
 
2.4%
p 14498
 
2.4%
Other values (3) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 99153
16.7%
e 99153
16.7%
D 84653
14.2%
o 84653
14.2%
u 84653
14.2%
b 84653
14.2%
i 14500
 
2.4%
T 14498
 
2.4%
r 14498
 
2.4%
p 14498
 
2.4%
Other values (3) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 99153
16.7%
e 99153
16.7%
D 84653
14.2%
o 84653
14.2%
u 84653
14.2%
b 84653
14.2%
i 14500
 
2.4%
T 14498
 
2.4%
r 14498
 
2.4%
p 14498
 
2.4%
Other values (3) 6
 
< 0.1%

Backhoe_Mounting
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing322453
Missing (%)80.4%
Memory size22.9 MiB
None or Unspecified
78652 
Yes
 
20

Length

Max length19
Median length19
Mean length18.995932
Min length3

Characters and Unicode

Total characters1494448
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 78652
 
19.6%
Yes 20
 
< 0.1%
(Missing) 322453
80.4%

Length

2024-07-25T13:59:49.588502image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:49.750429image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 78652
33.3%
or 78652
33.3%
unspecified 78652
33.3%
yes 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 235976
15.8%
o 157304
10.5%
n 157304
10.5%
157304
10.5%
i 157304
10.5%
s 78672
 
5.3%
N 78652
 
5.3%
r 78652
 
5.3%
U 78652
 
5.3%
p 78652
 
5.3%
Other values (4) 235976
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1494448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 235976
15.8%
o 157304
10.5%
n 157304
10.5%
157304
10.5%
i 157304
10.5%
s 78672
 
5.3%
N 78652
 
5.3%
r 78652
 
5.3%
U 78652
 
5.3%
p 78652
 
5.3%
Other values (4) 235976
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1494448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 235976
15.8%
o 157304
10.5%
n 157304
10.5%
157304
10.5%
i 157304
10.5%
s 78672
 
5.3%
N 78652
 
5.3%
r 78652
 
5.3%
U 78652
 
5.3%
p 78652
 
5.3%
Other values (4) 235976
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1494448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 235976
15.8%
o 157304
10.5%
n 157304
10.5%
157304
10.5%
i 157304
10.5%
s 78672
 
5.3%
N 78652
 
5.3%
r 78652
 
5.3%
U 78652
 
5.3%
p 78652
 
5.3%
Other values (4) 235976
15.8%

Blade_Type
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing321292
Missing (%)80.1%
Memory size22.0 MiB
PAT
38612 
Straight
13323 
None or Unspecified
11431 
Semi U
8617 
VPAT
 
3547
Other values (5)
4303 

Length

Max length19
Median length8
Mean length6.4810417
Min length1

Characters and Unicode

Total characters517401
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAT
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
PAT 38612
 
9.6%
Straight 13323
 
3.3%
None or Unspecified 11431
 
2.8%
Semi U 8617
 
2.1%
VPAT 3547
 
0.9%
U 1862
 
0.5%
Angle 1662
 
0.4%
No 743
 
0.2%
Landfill 25
 
< 0.1%
Coal 11
 
< 0.1%
(Missing) 321292
80.1%

Length

2024-07-25T13:59:49.946336image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:50.151592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
pat 38612
34.7%
straight 13323
 
12.0%
none 11431
 
10.3%
or 11431
 
10.3%
unspecified 11431
 
10.3%
u 10479
 
9.4%
semi 8617
 
7.7%
vpat 3547
 
3.2%
angle 1662
 
1.5%
no 743
 
0.7%
Other values (2) 36
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 44827
 
8.7%
e 44572
 
8.6%
A 43821
 
8.5%
P 42159
 
8.1%
T 42159
 
8.1%
31479
 
6.1%
t 26646
 
5.1%
r 24754
 
4.8%
n 24549
 
4.7%
o 23616
 
4.6%
Other values (16) 168819
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 44827
 
8.7%
e 44572
 
8.6%
A 43821
 
8.5%
P 42159
 
8.1%
T 42159
 
8.1%
31479
 
6.1%
t 26646
 
5.1%
r 24754
 
4.8%
n 24549
 
4.7%
o 23616
 
4.6%
Other values (16) 168819
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 44827
 
8.7%
e 44572
 
8.6%
A 43821
 
8.5%
P 42159
 
8.1%
T 42159
 
8.1%
31479
 
6.1%
t 26646
 
5.1%
r 24754
 
4.8%
n 24549
 
4.7%
o 23616
 
4.6%
Other values (16) 168819
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 44827
 
8.7%
e 44572
 
8.6%
A 43821
 
8.5%
P 42159
 
8.1%
T 42159
 
8.1%
31479
 
6.1%
t 26646
 
5.1%
r 24754
 
4.8%
n 24549
 
4.7%
o 23616
 
4.6%
Other values (16) 168819
32.6%

Travel_Controls
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing321291
Missing (%)80.1%
Memory size22.9 MiB
None or Unspecified
69923 
Differential Steer
 
4879
Finger Tip
 
2624
2 Pedal
 
1142
Lever
 
840
Other values (2)
 
426

Length

Max length19
Median length19
Mean length18.249656
Min length5

Characters and Unicode

Total characters1456943
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone or Unspecified
2nd rowNone or Unspecified
3rd rowNone or Unspecified
4th rowNone or Unspecified
5th rowNone or Unspecified

Common Values

ValueCountFrequency (%)
None or Unspecified 69923
 
17.4%
Differential Steer 4879
 
1.2%
Finger Tip 2624
 
0.7%
2 Pedal 1142
 
0.3%
Lever 840
 
0.2%
Pedal 416
 
0.1%
1 Speed 10
 
< 0.1%
(Missing) 321291
80.1%

Length

2024-07-25T13:59:50.425665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:50.623071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
none 69923
30.6%
or 69923
30.6%
unspecified 69923
30.6%
differential 4879
 
2.1%
steer 4879
 
2.1%
finger 2624
 
1.1%
tip 2624
 
1.1%
pedal 1558
 
0.7%
2 1142
 
0.5%
lever 840
 
0.4%
Other values (2) 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 235167
16.1%
i 154852
10.6%
148501
10.2%
n 147349
10.1%
o 139846
9.6%
r 83145
 
5.7%
f 79681
 
5.5%
p 72557
 
5.0%
d 71491
 
4.9%
N 69923
 
4.8%
Other values (16) 254431
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1456943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 235167
16.1%
i 154852
10.6%
148501
10.2%
n 147349
10.1%
o 139846
9.6%
r 83145
 
5.7%
f 79681
 
5.5%
p 72557
 
5.0%
d 71491
 
4.9%
N 69923
 
4.8%
Other values (16) 254431
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1456943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 235167
16.1%
i 154852
10.6%
148501
10.2%
n 147349
10.1%
o 139846
9.6%
r 83145
 
5.7%
f 79681
 
5.5%
p 72557
 
5.0%
d 71491
 
4.9%
N 69923
 
4.8%
Other values (16) 254431
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1456943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 235167
16.1%
i 154852
10.6%
148501
10.2%
n 147349
10.1%
o 139846
9.6%
r 83145
 
5.7%
f 79681
 
5.5%
p 72557
 
5.0%
d 71491
 
4.9%
N 69923
 
4.8%
Other values (16) 254431
17.5%

Differential_Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing331714
Missing (%)82.7%
Memory size22.0 MiB
Standard
68073 
Limited Slip
 
1130
No Spin
 
206
Locking
 
2

Length

Max length12
Median length8
Mean length8.0621227
Min length7

Characters and Unicode

Total characters559600
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 68073
 
17.0%
Limited Slip 1130
 
0.3%
No Spin 206
 
0.1%
Locking 2
 
< 0.1%
(Missing) 331714
82.7%

Length

2024-07-25T13:59:50.825986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:50.997034image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 68073
96.2%
limited 1130
 
1.6%
slip 1130
 
1.6%
no 206
 
0.3%
spin 206
 
0.3%
locking 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 137276
24.5%
a 136146
24.3%
S 69409
12.4%
t 69203
12.4%
n 68281
12.2%
r 68073
12.2%
i 3598
 
0.6%
p 1336
 
0.2%
1336
 
0.2%
L 1132
 
0.2%
Other values (8) 3810
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 559600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 137276
24.5%
a 136146
24.3%
S 69409
12.4%
t 69203
12.4%
n 68281
12.2%
r 68073
12.2%
i 3598
 
0.6%
p 1336
 
0.2%
1336
 
0.2%
L 1132
 
0.2%
Other values (8) 3810
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 559600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 137276
24.5%
a 136146
24.3%
S 69409
12.4%
t 69203
12.4%
n 68281
12.2%
r 68073
12.2%
i 3598
 
0.6%
p 1336
 
0.2%
1336
 
0.2%
L 1132
 
0.2%
Other values (8) 3810
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 559600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 137276
24.5%
a 136146
24.3%
S 69409
12.4%
t 69203
12.4%
n 68281
12.2%
r 68073
12.2%
i 3598
 
0.6%
p 1336
 
0.2%
1336
 
0.2%
L 1132
 
0.2%
Other values (8) 3810
 
0.7%

Steering_Controls
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing331756
Missing (%)82.7%
Memory size22.3 MiB
Conventional
68679 
Command Control
 
537
Four Wheel Standard
 
138
Wheel
 
14
No
 
1

Length

Max length19
Median length12
Mean length12.035592
Min length2

Characters and Unicode

Total characters834897
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowConventional
2nd rowConventional
3rd rowConventional
4th rowConventional
5th rowConventional

Common Values

ValueCountFrequency (%)
Conventional 68679
 
17.1%
Command Control 537
 
0.1%
Four Wheel Standard 138
 
< 0.1%
Wheel 14
 
< 0.1%
No 1
 
< 0.1%
(Missing) 331756
82.7%

Length

2024-07-25T13:59:51.190497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T13:59:51.377484image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
conventional 68679
97.9%
command 537
 
0.8%
control 537
 
0.8%
wheel 152
 
0.2%
four 138
 
0.2%
standard 138
 
0.2%
no 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 207249
24.8%
o 139108
16.7%
C 69753
 
8.4%
a 69492
 
8.3%
l 69368
 
8.3%
t 69354
 
8.3%
e 68983
 
8.3%
v 68679
 
8.2%
i 68679
 
8.2%
m 1074
 
0.1%
Other values (9) 3158
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 834897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 207249
24.8%
o 139108
16.7%
C 69753
 
8.4%
a 69492
 
8.3%
l 69368
 
8.3%
t 69354
 
8.3%
e 68983
 
8.3%
v 68679
 
8.2%
i 68679
 
8.2%
m 1074
 
0.1%
Other values (9) 3158
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 834897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 207249
24.8%
o 139108
16.7%
C 69753
 
8.4%
a 69492
 
8.3%
l 69368
 
8.3%
t 69354
 
8.3%
e 68983
 
8.3%
v 68679
 
8.2%
i 68679
 
8.2%
m 1074
 
0.1%
Other values (9) 3158
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 834897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 207249
24.8%
o 139108
16.7%
C 69753
 
8.4%
a 69492
 
8.3%
l 69368
 
8.3%
t 69354
 
8.3%
e 68983
 
8.3%
v 68679
 
8.2%
i 68679
 
8.2%
m 1074
 
0.1%
Other values (9) 3158
 
0.4%

Interactions

2024-07-25T13:59:08.324994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:58:59.605010image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:00.830444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:02.161973image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:03.415321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:05.186876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:06.638429image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:08.565932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:58:59.774113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.027542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:02.345970image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:03.602233image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:05.390443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:06.839787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:08.869284image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:58:59.955230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.223956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:02.528342image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:04.157056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:05.612753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:07.048598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:09.257137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:00.124452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.408759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:02.706159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:04.343313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:05.821748image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:07.257887image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:09.605524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:00.303538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.601628image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:02.890164image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:04.554333image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:06.036418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:07.466179image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:09.855144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:00.476010image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.793204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:03.076470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:04.771219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:06.246297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:07.708128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:10.128150image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:00.638334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:01.948209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:03.229368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:04.952215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:06.417968image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T13:59:08.036549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-25T13:59:51.648688image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Backhoe_MountingBlade_ExtensionBlade_TypeBlade_WidthCouplerCoupler_SystemDifferential_TypeDrive_SystemEnclosureEnclosure_TypeEngine_HorsepowerForksGrouser_TracksGrouser_TypeHydraulicsHydraulics_FlowMachineHoursCurrentMeterMachineIDModelIDPad_TypePattern_ChangerProductGroupProductGroupDescProductSizePushblockRide_ControlRipperSalePriceSalesIDScarifierSteering_ControlsStickStick_LengthThumbTip_ControlTire_SizeTrack_TypeTransmissionTravel_ControlsTurbochargedUndercarriage_Pad_WidthUsageBandYearMadeauctioneerIDdatasource
Backhoe_Mounting1.0000.0000.0210.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0001.0001.0001.0000.0000.0000.0050.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.000
Blade_Extension0.0001.0000.0000.0510.0000.0000.0000.0330.1000.1030.0990.0000.0000.0000.1440.0000.0000.0840.0840.0000.0001.0001.0000.0000.0170.0000.0130.0870.0930.0350.0000.0000.0000.0000.0000.0600.0000.0530.0000.0000.0000.1600.0160.0320.093
Blade_Type0.0210.0001.0000.0000.0000.0000.0000.0000.2750.0000.0000.0000.0000.0000.4730.0000.0000.1330.1650.0000.0001.0001.0000.5010.0000.0000.2400.1580.1320.0000.0000.0000.0000.0000.0000.0000.0000.1470.4640.0000.0000.1210.1080.0650.165
Blade_Width0.0000.0510.0001.0000.0000.0000.0000.0540.2360.1300.1540.0000.0000.0000.0550.0000.0000.0900.1610.0000.0001.0001.0000.0000.3660.0000.3540.1550.1140.2230.0000.0000.0000.0000.1120.3300.0000.0460.0000.0000.0000.1990.0950.1290.105
Coupler0.0000.0000.0000.0001.0000.7050.0870.0000.0930.0000.0000.3280.1810.0210.2060.0880.0060.1440.0820.0000.1140.0770.0770.0950.0000.0870.0000.0890.1270.0000.0180.0000.0640.1150.0000.1720.1270.0000.0000.0000.0630.0470.0510.0450.181
Coupler_System0.0000.0000.0000.0000.7051.0000.0000.0000.1140.0000.0000.0460.2010.0000.3400.0960.0000.2290.1550.0000.0001.0001.0000.0000.0000.0000.0000.0210.2660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0350.0780.537
Differential_Type0.0000.0000.0000.0000.0870.0001.0000.0000.0820.0000.0000.0750.0000.0000.1650.0000.0000.1470.0330.0000.0001.0001.0000.0390.0000.1530.0000.0700.0510.0000.0160.0000.0000.0000.0000.2340.0000.0000.0000.0000.0000.0740.0310.0140.047
Drive_System0.0000.0330.0000.0540.0000.0000.0001.0000.3180.0610.0260.1240.0000.0000.1060.0000.0000.1770.2260.2350.0001.0001.0000.0000.0330.0450.0450.4120.0750.0210.0000.2650.0000.0000.0600.0830.0000.5690.0000.1640.0000.1090.0950.0460.085
Enclosure0.0060.1000.2750.2360.0930.1140.0820.3181.0000.2820.2780.1980.1900.0690.2190.0920.0000.1020.1030.0830.1520.3030.3030.2720.2330.3610.2360.2220.0910.0610.0810.3070.2330.0750.1270.1910.1980.2700.2680.0800.0880.1690.0790.0410.117
Enclosure_Type0.0000.1030.0000.1300.0000.0000.0000.0610.2821.0000.2990.0000.0000.0000.2570.0000.0000.2910.2730.0000.0001.0001.0000.0000.1720.0000.1580.2930.1490.1020.0000.0000.0000.0000.0930.1360.0000.0440.0000.0000.0000.1800.0830.0430.149
Engine_Horsepower0.0000.0990.0000.1540.0000.0000.0000.0260.2780.2991.0000.0000.0000.0000.2730.0000.0000.2790.3970.0000.0001.0001.0000.0000.1690.0000.1800.3280.1300.0340.0000.0000.0000.0000.0400.1180.0000.0950.0000.0000.0000.2030.0540.0240.144
Forks0.0000.0000.0000.0000.3280.0460.0750.1240.1980.0000.0001.0000.0490.0000.2860.0310.0000.1680.0830.1520.0000.2110.2110.0680.0000.2000.0000.1990.1100.0000.0130.0820.0000.0000.0000.1550.0000.0660.0000.0200.0000.0620.0220.0160.113
Grouser_Tracks0.0000.0000.0000.0000.1810.2010.0000.0000.1900.0000.0000.0491.0000.0000.3010.0900.0000.2240.1790.0000.0001.0001.0000.0000.0000.0000.0000.0500.2320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0230.0520.355
Grouser_Type0.0000.0000.0000.0000.0210.0000.0000.0000.0690.0000.0000.0000.0001.0000.0410.0000.0200.1350.1010.0000.0941.0001.0000.1280.0000.0000.0000.0980.0640.0000.0000.0000.0760.0650.0000.0000.1530.0000.0000.0000.2530.0770.0380.0810.055
Hydraulics0.0000.1440.4730.0550.2060.3400.1650.1060.2190.2570.2730.2860.3010.0411.0000.1090.0000.0910.1700.0000.1390.6990.6990.4470.1370.1110.3460.1190.1730.0540.5910.0000.0370.1380.1560.3720.2650.4420.5290.0000.1910.0980.0800.0490.154
Hydraulics_Flow0.0000.0000.0000.0000.0880.0960.0000.0000.0920.0000.0000.0310.0900.0000.1091.0000.0000.0460.0570.0000.0001.0001.0000.0000.0000.0000.0000.0370.0860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0190.103
MachineHoursCurrentMeter0.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0001.000-0.0780.0430.0160.0040.0040.0040.0000.0000.0040.0140.118-0.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0760.0800.1870.0970.018
MachineID0.0000.0840.1330.0900.1440.2290.1470.1770.1020.2910.2790.1680.2240.1350.0910.046-0.0781.0000.1900.3620.4010.0580.0580.1000.1860.1680.114-0.1840.3440.1730.0460.1390.0860.1820.1660.0640.0460.0710.1490.2340.0610.129-0.038-0.1090.506
ModelID0.0160.0840.1650.1610.0820.1550.0330.2260.1030.2730.3970.0830.1790.1010.1700.0570.0430.1901.0000.1990.1740.2650.2650.2640.2600.2610.164-0.2630.1910.1830.0310.1600.0980.0790.1700.1700.2590.1120.1160.1370.0800.1090.096-0.0150.117
Pad_Type0.0000.0000.0000.0000.0000.0000.0000.2350.0830.0000.0000.1520.0000.0000.0000.0000.0160.3620.1991.0000.0001.0001.0000.0000.0000.0920.0000.1720.0820.0000.0000.1310.0000.0000.0000.0000.0000.0430.0000.3010.0000.1150.0520.0260.076
Pattern_Changer0.0000.0000.0000.0000.1140.0000.0000.0000.1520.0000.0000.0000.0000.0940.1390.0000.0040.4010.1740.0001.0001.0001.0000.0560.0000.0000.0000.1570.1580.0000.0000.0000.2150.0530.0000.0000.0910.0000.0000.0000.0880.0630.0990.0230.150
ProductGroup1.0001.0001.0001.0000.0771.0001.0001.0000.3031.0001.0000.2111.0001.0000.6991.0000.0040.0580.2651.0001.0001.0001.0000.7781.0000.9790.5260.2660.1611.0001.0001.0001.0001.0001.0000.7121.0000.7051.0001.0001.0000.0790.0750.0390.061
ProductGroupDesc1.0001.0001.0001.0000.0771.0001.0001.0000.3031.0001.0000.2111.0001.0000.6991.0000.0040.0580.2651.0001.0001.0001.0000.7781.0000.9790.5260.2660.1611.0001.0001.0001.0001.0001.0000.7121.0000.7051.0001.0001.0000.0790.0750.0390.061
ProductSize1.0000.0000.5010.0000.0950.0000.0390.0000.2720.0000.0000.0680.0000.1280.4470.0000.0000.1000.2640.0000.0560.7780.7781.0000.0000.1110.2220.2660.1580.0000.0510.0000.3090.0770.0000.3630.6620.0940.2820.0000.3370.2660.1200.0350.082
Pushblock0.0000.0170.0000.3660.0000.0000.0000.0330.2330.1720.1690.0000.0000.0000.1370.0000.0000.1860.2600.0000.0001.0001.0000.0001.0000.0000.6090.3710.1340.1870.0000.0000.0000.0000.0140.3140.0000.0880.0000.0000.0000.2010.0790.1000.131
Ride_Control0.0000.0000.0000.0000.0870.0000.1530.0450.3610.0000.0000.2000.0000.0000.1110.0000.0040.1680.2610.0920.0000.9790.9790.1110.0001.0000.0000.3880.1510.0000.7120.1410.0000.0000.0000.2140.0000.2110.0000.0000.0000.1190.0780.0490.148
Ripper0.0050.0130.2400.3540.0000.0000.0000.0450.2360.1580.1800.0000.0000.0000.3460.0000.0140.1140.1640.0000.0000.5260.5260.2220.6090.0001.0000.1900.1100.3090.0000.0000.0000.0000.0280.2820.0000.2980.0980.0000.0000.0630.0450.0480.105
SalePrice0.0150.0870.1580.1550.0890.0210.0700.4120.2220.2930.3280.1990.0500.0980.1190.0370.118-0.184-0.2630.1720.1570.2660.2660.2660.3710.3880.1901.000-0.0610.1150.0850.2120.1290.0640.1180.1400.4340.1330.1700.0900.0990.1690.245-0.0620.043
SalesID0.0000.0930.1320.1140.1270.2660.0510.0750.0910.1490.1300.1100.2320.0640.1730.086-0.0810.3440.1910.0820.1580.1610.1610.1580.1340.1510.110-0.0611.0000.0610.0460.1270.0520.1760.1330.0770.1330.0450.1420.0670.0870.0440.230-0.2390.782
Scarifier0.0000.0350.0000.2230.0000.0000.0000.0210.0610.1020.0340.0000.0000.0000.0540.0000.0000.1730.1830.0000.0001.0001.0000.0000.1870.0000.3090.1150.0611.0000.0000.0000.0000.0000.0990.2190.0000.0360.0000.0000.0000.0280.0000.0160.057
Steering_Controls0.0000.0000.0000.0000.0180.0000.0160.0000.0810.0000.0000.0130.0000.0000.5910.0000.0000.0460.0310.0000.0001.0001.0000.0510.0000.7120.0000.0850.0460.0001.0000.0000.0000.0000.0000.3600.0000.0000.0000.0000.0000.0550.0200.0150.044
Stick0.0000.0000.0000.0000.0000.0000.0000.2650.3070.0000.0000.0820.0000.0000.0000.0000.0000.1390.1600.1310.0001.0001.0000.0000.0000.1410.0000.2120.1270.0000.0001.0000.0000.0000.0000.0000.0000.0700.0000.0690.0000.1120.0300.0790.119
Stick_Length0.0000.0000.0000.0000.0640.0000.0000.0000.2330.0000.0000.0000.0000.0760.0370.0000.0000.0860.0980.0000.2151.0001.0000.3090.0000.0000.0000.1290.0520.0000.0000.0001.0000.0730.0000.0000.1990.0000.0000.0000.0680.1430.0780.0560.047
Thumb0.0000.0000.0000.0000.1150.0000.0000.0000.0750.0000.0000.0000.0000.0650.1380.0000.0000.1820.0790.0000.0531.0001.0000.0770.0000.0000.0000.0640.1760.0000.0000.0000.0731.0000.0000.0000.0750.0000.0000.0000.0760.0490.0420.0500.179
Tip_Control0.0000.0000.0000.1120.0000.0000.0000.0600.1270.0930.0400.0000.0000.0000.1560.0000.0000.1660.1700.0000.0001.0001.0000.0000.0140.0000.0280.1180.1330.0990.0000.0000.0000.0001.0000.0960.0000.0210.0000.0000.0000.0000.1220.0910.174
Tire_Size0.0000.0600.0000.3300.1720.0000.2340.0830.1910.1360.1180.1550.0000.0000.3720.0000.0000.0640.1700.0000.0000.7120.7120.3630.3140.2140.2820.1400.0770.2190.3600.0000.0000.0000.0961.0000.0000.0390.0000.0000.0000.2030.0450.0900.096
Track_Type0.0000.0000.0000.0000.1270.0000.0000.0000.1980.0000.0000.0000.0000.1530.2650.0000.0000.0460.2590.0000.0911.0001.0000.6620.0000.0000.0000.4340.1330.0000.0000.0000.1990.0750.0000.0001.0000.0000.0000.0000.1890.3020.0490.0530.098
Transmission0.0000.0530.1470.0460.0000.0000.0000.5690.2700.0440.0950.0660.0000.0000.4420.0000.0000.0710.1120.0430.0000.7050.7050.0940.0880.2110.2980.1330.0450.0360.0000.0700.0000.0000.0210.0390.0001.0000.1540.0220.0000.0800.1090.0970.205
Travel_Controls0.0000.0000.4640.0000.0000.0000.0000.0000.2680.0000.0000.0000.0000.0000.5290.0000.0000.1490.1160.0000.0001.0001.0000.2820.0000.0000.0980.1700.1420.0000.0000.0000.0000.0000.0000.0000.0000.1541.0000.0000.0000.1190.1430.0470.151
Turbocharged0.0000.0000.0000.0000.0000.0000.0000.1640.0800.0000.0000.0200.0000.0000.0000.0000.0310.2340.1370.3010.0001.0001.0000.0000.0000.0000.0000.0900.0670.0000.0000.0690.0000.0000.0000.0000.0000.0220.0001.0000.0000.0600.0330.0120.027
Undercarriage_Pad_Width0.0000.0000.0000.0000.0630.0000.0000.0000.0880.0000.0000.0000.0000.2530.1910.0000.0760.0610.0800.0000.0881.0001.0000.3370.0000.0000.0000.0990.0870.0000.0000.0000.0680.0760.0000.0000.1890.0000.0000.0001.0000.0810.0330.0820.118
UsageBand0.0000.1600.1210.1990.0470.0190.0740.1090.1690.1800.2030.0620.0530.0770.0980.0000.0800.1290.1090.1150.0630.0790.0790.2660.2010.1190.0630.1690.0440.0280.0550.1120.1430.0490.0000.2030.3020.0800.1190.0600.0811.0000.1910.0460.049
YearMade0.0000.0160.1080.0950.0510.0350.0310.0950.0790.0830.0540.0220.0230.0380.0800.0140.187-0.0380.0960.0520.0990.0750.0750.1200.0790.0780.0450.2450.2300.0000.0200.0300.0780.0420.1220.0450.0490.1090.1430.0330.0330.1911.000-0.1550.116
auctioneerID0.0110.0320.0650.1290.0450.0780.0140.0460.0410.0430.0240.0160.0520.0810.0490.0190.097-0.109-0.0150.0260.0230.0390.0390.0350.1000.0490.048-0.062-0.2390.0160.0150.0790.0560.0500.0910.0900.0530.0970.0470.0120.0820.046-0.1551.0000.098
datasource0.0000.0930.1650.1050.1810.5370.0470.0850.1170.1490.1440.1130.3550.0550.1540.1030.0180.5060.1170.0760.1500.0610.0610.0820.1310.1480.1050.0430.7820.0570.0440.1190.0470.1790.1740.0960.0980.2050.1510.0270.1180.0490.1160.0981.000

Missing values

2024-07-25T13:59:11.442608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-25T13:59:15.592187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-25T13:59:24.012599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SalesIDSalePriceMachineIDModelIDdatasourceauctioneerIDYearMadeMachineHoursCurrentMeterUsageBandsaledatefiModelDescfiBaseModelfiSecondaryDescfiModelSeriesfiModelDescriptorProductSizefiProductClassDescstateProductGroupProductGroupDescDrive_SystemEnclosureForksPad_TypeRide_ControlStickTransmissionTurbochargedBlade_ExtensionBlade_WidthEnclosure_TypeEngine_HorsepowerHydraulicsPushblockRipperScarifierTip_ControlTire_SizeCouplerCoupler_SystemGrouser_TracksHydraulics_FlowTrack_TypeUndercarriage_Pad_WidthStick_LengthThumbPattern_ChangerGrouser_TypeBackhoe_MountingBlade_TypeTravel_ControlsDifferential_TypeSteering_Controls
011392466600099908931571213.0200468.0Low11/16/2006 0:00521D521DNaNNaNNaNWheel Loader - 110.0 to 120.0 HorsepowerAlabamaWLWheel LoaderNaNEROPS w ACNone or UnspecifiedNaNNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNone or UnspecifiedNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional
1113924857000117657771213.019964640.0Low3/26/2004 0:00950FII950FIINaNMediumWheel Loader - 150.0 to 175.0 HorsepowerNorth CarolinaWLWheel LoaderNaNEROPS w ACNone or UnspecifiedNaNNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaN23.5None or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional
211392491000043480870091213.020012838.0High2/26/2004 0:00226226NaNNaNNaNNaNSkid Steer Loader - 1351.0 to 1601.0 Lb Operating CapacityNew YorkSSLSkid Steer LoadersNaNOROPSNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
311392513850010264703321213.020013486.0High5/19/2011 0:00PC120-6EPC120NaN-6ENaNSmallHydraulic Excavator, Track - 12.0 to 14.0 Metric TonsTexasTEXTrack ExcavatorsNaNEROPS w ACNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41139253110001057373173111213.02007722.0Medium7/23/2009 0:00S175S175NaNNaNNaNNaNSkid Steer Loader - 1601.0 to 1751.0 Lb Operating CapacityNew YorkSSLSkid Steer LoadersNaNEROPSNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5113925526500100127446051213.02004508.0Low12/18/2008 0:00310G310GNaNNaNNaNBackhoe Loader - 14.0 to 15.0 Ft Standard Digging DepthArizonaBLBackhoe LoadersFour Wheel DriveOROPSNone or UnspecifiedNone or UnspecifiedNoExtendedPowershuttleNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
611392562100077270119371213.0199311540.0High8/26/2004 0:00790ELC790ENaNLCLarge / MediumHydraulic Excavator, Track - 21.0 to 24.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
711392612700090200235391213.020014883.0High11/17/2005 0:00416D416DNaNNaNNaNBackhoe Loader - 14.0 to 15.0 Ft Standard Digging DepthIllinoisBLBackhoe LoadersFour Wheel DriveOROPSNone or UnspecifiedReversibleNoStandardStandardYesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
81139272215001036251360031213.02008302.0Low8/27/2009 0:00430HAG430HAGNaNNaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsTexasTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNManualNaNNaNNaNRubberNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
9113927565000101647438831213.0100020700.0Medium8/9/2007 0:00988B988BNaNNaNLargeWheel Loader - 350.0 to 500.0 HorsepowerFloridaWLWheel LoaderNaNEROPS w ACNone or UnspecifiedNaNNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNone or UnspecifiedNone or UnspecifiedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional
SalesIDSalePriceMachineIDModelIDdatasourceauctioneerIDYearMadeMachineHoursCurrentMeterUsageBandsaledatefiModelDescfiBaseModelfiSecondaryDescfiModelSeriesfiModelDescriptorProductSizefiProductClassDescstateProductGroupProductGroupDescDrive_SystemEnclosureForksPad_TypeRide_ControlStickTransmissionTurbochargedBlade_ExtensionBlade_WidthEnclosure_TypeEngine_HorsepowerHydraulicsPushblockRipperScarifierTip_ControlTire_SizeCouplerCoupler_SystemGrouser_TracksHydraulics_FlowTrack_TypeUndercarriage_Pad_WidthStick_LengthThumbPattern_ChangerGrouser_TypeBackhoe_MountingBlade_TypeTravel_ControlsDifferential_TypeSteering_Controls
4011156333290100001843374214371492.02005NaNNaN10/25/2011 0:0035N35NNaNNaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
401116633330285001825337214371492.02005NaNNaN10/25/2011 0:0035N35NNaNNaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
4011176333307100001821747214371492.02005NaNNaN10/25/2011 0:0035N35NNaNNaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
401118633331195001828862214371492.02006NaNNaN10/25/2011 0:0035N35NNaNNaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
401119633333585001798293214351492.02005NaNNaN10/25/2011 0:0030NX30NXNaNNaNMiniHydraulic Excavator, Track - 2.0 to 3.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
4011206333336105001840702214391491.02005NaNNaN11/2/2011 0:0035NX235NX2NaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsMarylandTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
4011216333337110001830472214391491.02005NaNNaN11/2/2011 0:0035NX235NX2NaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsMarylandTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
4011226333338115001887659214391491.02005NaNNaN11/2/2011 0:0035NX235NX2NaNMiniHydraulic Excavator, Track - 3.0 to 4.0 Metric TonsMarylandTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
401123633334190001903570214351492.02005NaNNaN10/25/2011 0:0030NX30NXNaNNaNMiniHydraulic Excavator, Track - 2.0 to 3.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN
401124633334277501926965214351492.02005NaNNaN10/25/2011 0:0030NX30NXNaNNaNMiniHydraulic Excavator, Track - 2.0 to 3.0 Metric TonsFloridaTEXTrack ExcavatorsNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNone or UnspecifiedNaNNaNNaNSteelNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedNone or UnspecifiedDoubleNaNNaNNaNNaNNaN